INTRODUCTION - UCL Computer Science - Home€¦ · Across UCL CMIC scientists are participating in an initiative to bring together all scientists interested and working in the development

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INTRODUCTION

The Centre for Medical Image Computing The last two years have seen significant changes as the Centre has developed. Most importantly we have now started to occupy the dedicated space allocated to us on the third floor of the Front Engineering Building on the corner of Malet Place and Torrington Place. This is an excellent facility and we are looking forward to the move to the rest of the accommodation in Spring 2011. This will accommodate about 38 researchers in total. In addition we have established satellites at the Dementia Research Centre (DRC) at the Institute of Neurology, the preclinical facilities at the Centre for Advanced Biomedical Imaging (CABI) and in Imaging Sciences at UCLH. Our complement with this year’s DTP intake of students is now nearly 70 and our annual research income from an increasingly diverse range of sponsors is £3.5M. Our main research activities continue to be applied to neurosciences and oncology, but with increasing activity in cardiovascular sciences. We are making significant impact in neurosciences, in particular in the assessment of dementias including our involvement with the ADNI consortium, and in large projects associated with screening for colon cancer and breast cancer and with minimally invasive treatments of prostate cancer. While our main effort is towards the translational pipeline and introduction of novel imaging, computation and modelling to healthcare, we have also been developing technologies of more fundamental nature in understanding microstructure and its application to connectivity in the brain, in particular as part of the CONNECT consortium, and in the development of reconstruction methods relating nuclear, x-ray and optical methods and using these to assess of changes indicative of disease.

We are part of the EPSRC/CR-UK Comprehensive Cancer Imaging Centre between KCL and UCL that has brought together a multidisciplinary group of scientists working from basic sciences, to preclinical models and clinical trials in the challenges of developing and assessing new treatments for cancer. We are also participants in two major NIHR funded programmes in colon cancer and prostate cancer where we provide novel imaging solutions to understand better the signals from MR and CT in screening and diagnosis. A major CBRC grant enabled us to start developing a suite of software, available to the research community as Open Source releases, but crucially enabling the non-computer expert (clinician or life scientist) to take advantage of our developments. Packages covering image registration (NiftyReg), segmentation (NiftySeg), biomechanical simulation (NiftySim) are already released and a viewer (NiftyView) will be released by Christmas. Visit http://www.ucl.ac.uk/cmic/software/ to download these tools. We have EU support for projects in breast cancer imaging, liver cancer treatment, studying connectivity in the brain and solving inverse problems in medical imaging. We also continue to support a variety of other widely used software toolkits including the Camino diffusion MRI toolkit www.camino.org.uk and DTITK www.nitrc.org/projects/dtitk, as well as involvement in ITK Snap www.itksnap.org.

In August this year we were delighted to be awarded an EPSRC Programme Grant “Intelligent Imaging: Structure, Form and Function Across Scale”, with Imperial, KCL and the Institute of Cancer Research as partners, which will enable us to carry out more long term and fundamental research into modelling tissue and organ motion and deformation and imaging moving structure in order to extract clinically relevant information on biology and structure across scale.

Within UCLH there are several major initiatives in which CMIC are involved including the development and installation, in the new Cancer Centre, of the first whole body combined PET/MRI facility in the UK (and one of the first in the world), development of a new Centre for Image Directed Healthcare and plans for the installation of the Proton Therapy Centre.

In Life Sciences we have shown how registration and atlas building technology can be used to develop extremely high resolution atlases of mammalian foetuses in the study of a wide range of congenital disorders with the CABI and the Institute of Child Health. We are involved in planning imaging facilities for the major UKCMRI biomedical research facility at St. Pancras.

Across UCL CMIC scientists are participating in an initiative to bring together all scientists interested and working in the development of new imaging technologies and methods. Last year we launched the Doctoral Training Programme in Medical and Biomedical Imaging, a 4 year, MRes + PhD programme integrating training in advanced imaging methods and a PhD research project. It is extremely popular and we have recruited 12 students to both the 2009 and 2010 intake. We are looking to build a sustainable model based on EPSRC and MRC DTAs, specific project studentships and overseas students.

Individual achievements since 2008 include an EPSRC Challenge Engineering Fellowship to Daniel Alexander, who was conferred the title of Professor of Imaging Science in 2009; an EPSRC Fellowship to Marta Betke; award of the FRCR Crookshank Medal and NIHR Senior Investigator status to Dave Hawkes. His post is now jointly funded by the Faculties of Engineering and Biomedicine.

Centre for Medical Image Computing

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We were awarded an EPSRC Platform Grant in Medical Image Computing shortly after CMIC was formed in 2005. In August 2010 we were delighted to be awarded an EPSRC Programme Grant “Intelligent Imaging: Motion, Structure and Form across Scale”. The former, together with the MIAS-IRC Interdisciplinary Research Collaboration funded by EPSRC and MRC, enabled us to develop the ideas for the EPSRC Programme.

The platform grant was used to build up a substantial joint research programme as a partnership between science, engineering and clinical researchers. Strategically we planned to tackle three main themes over 5 years:

1. Intelligent image acquisition coupling recent innovations in image acquisition to novel reconstruction methods that use prior knowledge of patient anatomy and motion

2. Modelling tissue structure, motion and growth patterns across scale in health and disease.

3. Applying these advances to image guided interventions.

This Platform has enabled us to address all 3 themes and in particular enabled us to bid successfully for a Programme Grant in 2009. This was awarded in August 2010.

This EPSRC Programme aims to

Intelligent Imaging: Motion,Structure and Form across Scale:An EPSRC Programme

change the way medical imaging is currently used in applications where quantitative assessment of disease progression or guidance of treatment is required. Imaging technology traditionally sees the reconstructed image as the end goal, but in reality it is a stepping stone to evaluate some aspect of the state of the patient, which we term the target, e.g. the presence, location, extent and characteristics of a particular disease, function of the heart, response to treatment etc. The image is merely an intermediate visualization, for subsequent interpretation and processing either by the human expert or computer based analysis. Our objectives are to extract information which can be used to inform diagnosis and guide therapy directly from the measurements of the imaging device. We propose a new paradigm whereby the extraction of clinically-relevant information drives the entire imaging process.

All medical imaging devices measure some physical attribute of the patient’s body, such as the X-ray attenuation in CT, changes in acoustic impedance in ultrasound, or the mobility of protons in MRI. These physical attributes may be modulated by changes in structure or metabolic function. Medical images from devices such as MR and CT scanners often take 10s of seconds to many minutes to acquire. The unborn child, the very young, the very old or very ill cannot stay still for this time and methods of addressing motion are inefficient and cannot be applied to all types of imaging. Usually triggering and gating strategies are applied, which result in a low acquisition efficiency (since most of the data is rejected) and often fail due to irregular motion. As a result the images are corrupted by significant motion artifact or blurring.

Accurate computational modeling of physiology and pathological processes at different spatial scales has shown how careful measurements from imaging devices might allow the clinician or the medical scientist to

infer what is happening in health, in specific diseases and during therapy. Unfortunately, making these accurate measurements is very difficult due to the movement artifacts described above.

Imaging systems can provide the therapist, interventionist or surgeon with a 3D navigational map showing where therapy should be delivered and measuring how effective it is. Unfortunately image guided interventions in the mov-ing and deforming tissues of the chest and abdomen are very difficult as the images are often corrupted by motion and as the procedure progresses the images generally diverge from the local anatomy that the interventionist or surgeon is treating.

Our programme brings together three different groups of people: computer scientists who construct computer models of anatomy, physiology, pharmacological processes and the dynamics of tissue motion; imaging scientists who develop new ways to reconstruct images of the human body; and clinicians working to provide better treatment for their patients. With these three groups working together we will devise new ways to correct for motion artifact, to produce images of optimal quality that are related directly to clinically relevant measures of tissue composition, microscopic structure and metabolism. We will apply these methods to improve understanding of disease progression; guide therapies and assess response to treatment in cancer arising in the lung and liver; to ischaemic heart disease; to the clinical management of the foetus while still in the womb; and to caring for premature babies and young children.

The Programme comprises researchers from Imperial (Jo Hajnal and Daniel Rueckert), KCL (Reza Razavi and Philip Batchelor), ICR (Martin Leach and Steve Webb) and UCL. (Dave Hawkes, David Atkinson, Daniel Alexander, Seb Ourselin and Ron Gaston as Project Manager.

Director: Dave Hawkes

CMIC Investigators

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Computational Imaging in Oncology

My own research is mainly focused on the development of novel computational imaging methods in oncology and their translation to clinical practice. I am particularly interested in developing improved methods for cancer screening and diagnosis and image guided interventions. I am currently PI of grants directed to the diagnosis and treatment of cancers of the breast, colon, lung and oesophagus and with Dean Barratt in liver and prostate cancer. Methodological research includes developments in image registration, integrating optical imaging with medical imaging, motion modeling and biomechanical modeling. I am particularly interested in how we might use medical imaging technologies to infer microscopic changes associated with cancer growth or response to treatment.

to improve dose delivery, by sparing adjacent normal structures. This work is supported by the EPSRC/CR-UK Comprehensive Cancer Imaging Centre of UCL and KCL, VisionRT and the Programme Grant.

Oesophageal Cancer: Mingxing Hu and Baptiste Allain, in collaboration with Lawrence Lovat of UCLH and Mauna Kea Technologies, have developed a novel method to aid optical biopsy and subsequent tissue sampling in the management of Barrett’s Oesophagus, a benign transformation that often precedes malignant disease. We are exploring non-invasive optical methods in conjunction with medical imaging to perform “in-vivo histology”.

Liver Cancer: Steve Thompson, Mingxing Hu and Stian Johnsen are working with Professor Brian Davison to develop a novel image guided endoscopy system to extend laparoscopic surgery in patients with metastatic and primary liver cancer.

Dave Hawkes

Selected publicationsWhite MJ, Hawkes DJ, et al “Motion artefact correction in free-breathing abdominal MRI using overlapping partial samples to recover imaging deformations” Magn Reson Imag (2009) 62:440-449 Melbourne A, Atkinson D, et al, “Registration of dynamic contrast enhanced MRI using progressive principle component registration (PPCR)”, Phys Med Biol (2007), 52: 5147-5156 Hipwell J, Tanner C, et al, “A new validation method for validation of X-ray Mammogram Registration Algorithms using a Projection Model of Breast X-ray Compression”, IEEE Tran Med Imag (2007), 26:1190-1200 McClelland et al Inter-fraction variations in respiratory motion models, Phys Med Biol (in press) Tanner C, et al Large Breast Compressions - Observations and Evaluation of Simulations, Medical Physics in press

X-ray mammogram (left) and corresponding MR volume (centre) deformed so that its projection (right) aligns with the mammogram

Breast cancer: as part of the EUFP7HAMAM project to develop a multimodal workstation, John Hipwell and Thomy Mertzanidou have developed a novel way how to register and fuse dynamic contrast enhance (DCE) MRI and digital x-ray mammograms based on an iterative deformation and forward modeling approach. This draws on work by Lianghao Han on fast breast tissue deformation modeling using a novel GPU based explicit finite element approach and work by John Hipwell to simulate X-ray mammographic imaging from segmented DCE MRI. This work is now being extended to provide image guided lumpectomy with Mo Keshtgar (Surgery). With Simon Arridge we have developed a combined reconstruction and registration approach for monitoring normal and pathological change in breast structure using serial Digital X-ray Tomosynthesis with Dexela Ltd. With Yassir Jafar we are using feature analysis and statistical image decomposition to monitor change in serial X-ray mammograms.

Colon Cancer: We are working with MedicEndo, together with support from a NIHR Programme Grant (Steve Halligan, Academic Radiology) and the joint EPSRC/CR-UK Comprehensive Cancer Imaging Centre (CCIC) with KCL to fuse information from prone and supine CT scans used for screening for colon cancer with colonoscopy views to better identify polyps and other early indications of colon cancer. Holger Roth, Jamie McClelland, Mingxing Hu and Tom Hampshire are devising new methods based on cylindrical registration of this tubular structure together with feature matching. Clinical testing with Darren Boone (clinical research fellow) and Brian Saunders (St. Mark’s) is in progress.

Lung Cancer: Jamie McClelland and James Martin, in collaboration with the Institute of Cancer Research, Guy’s and St. Thomas’ Hospital and UCLH, are developing methods to model respiratory motion of the lung, tumour and adjacent structures from 4D CT and cone-beam CT. The aim is

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GrantsIntelligent Imaging: Motion, Form And Function Across ScaleEPSRC EP/H046410/1 01/06/2010 to 31/05/2015 £5,974,098

The UCL Centre For Medical Image Computing EPSRC EP/D506468/1 24/11/2005 to 31/07/2011 £382,177

Registration Of Prone And Supine Ct For Colonography And Establishing Correspondence With Colonoscopy To Aid Polyp IdentificationMedicendo Ltd 01/09/2008 to 30/09/2011 £389,669

Digital Breast TomosynthesisEPSRC DT/F002785/1 01/09/2007 to 14/02/2011 £267,712

Transcostal High Intensity Focused Ultrasound For The Treatment Of Cancer EPSRC EP/F025750/1 19/02/2008 to 22/06/2013 £516,979

Passport - Patient Specific Simulation And Preoperative Realistic Training For Liver Surgery European Commission FP7 223894 01/06/2008 to 31/05/2011 £245,161

Hamam - Highly Accurate Breast Cancer Diagnosis Through Integration Of Biological Knowledge, Novel Imaging Modalities And ModellingEuropean Commission FP7 224538 01/09/2008 to 31/08/2011 £394,618

Comprehensive Cancer Imaging Centre Cancer Research UK and EPSRC MOTTBPR-NG-CRUK01/12/2008 to 30/11/2013 £475,892

Post-graduate students•Baptiste Allain •Yassir Jafar •Thomy Mertzanidou •Nayereh Seyedarabi •Holger Roth •Tom Hampshire •James Martin •Bjoern Eiben

Research Associates: •John Hipwell •Mingxing Hu •Christine Tanner (joint with ETH Zurich) •Tim Carter •Jamie McClelland •Lianghao Han •Stian Johnsen

Dave Hawkes

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Enhancing Translation from Bench to Bedside using Computational Imaging

Our research is mainly focused on the development of novel imaging biomarkers. We are taking advantage of the breath of algorithms developed within CMIC and the internationally leading clinical research at UCL to design robust and innovative software for clinical trials (Phase 2 and 3). Robust software plays a critical role in clinical trials and will result in improved healthcare delivery to patients.

the Dementia Research Centre (N. Fox, M. Rossor) and IXICO (D. Hill) we are developing novel imaging biomarkers for Alzheimer’s disease using MR and PET images, enabling the robust and automatic measure of disease progression and disease modification. We have been focusing on whole brain atrophy (K. Leung), hippocampus (J. Barnes, K. Leung), cortical thickness (M. Clarkson, J. Cardoso), longitudnal analysis (M. Modat), DTI (S.Keihaninejad) and more recently PIB uptake (A. Andrews) and classification (J. Young). These imaging biomarkers are currently used in many clinical trials, including large international activities, such as the ADNI and DIAN initiatives.

iMRI Neurosurgery: Being able to obtain high quality MR images during surgery is opening new opportunities to update the surgical planning during the surgery. In collaboration with the IoN (T. Yousry, J. Thornton) and NHNN (A. McEvoy), we are developing near real-time registration algorithms to combine pre-operative images with intra-operative images (P. Daga, M. Modat). This is opening exciting opportunities to improve surgical outcomes, especially in epilepsy surgery (J. Duncan, G. Watson).

Improving PET and SPECT image reconstruction: PET and SPECT are functional imaging techniques that find application in diagnosis of ischemic heart disease, mapping of local brain metabolism, drug development and detection of tumors and areas of infection. In collaboration with S. Arridge and INM (B. Hutton), we are investigating the correlation of drug distribution with the underlying tissue morphology and consequently the use of information from other imaging modalities, such as MRI and CT, to improve the quantification of pharmaceutical distribution. (S. Pedemonte, D. Kazantsev).

Phenotyping: In collaboration with our team, CABI (M. Lythoge) develops imaging techniques to investigate the anatomical, structural and functional contributions of genes to human diseases and development

Sebastien Ourselin

Selected publicationsLeung KK, Barnes J, Ridgway GR, Bartlett JW, Clarkson MJ, Macdonald K, Schuff N, Fox NC, Ourselin S. “Automated cross-sectional and longitudinal hippocampal volume measurement in mild cognitive impairment and Alzheimer’s disease. “ Neuroimage. 2010 Jul 15;51(4):1345-59.

M. Modat, G.R. Ridgway, Z.A. Taylor, M. Lehmann, J. Barnes, D.J. Hawkes, N.C. Fox, S. Ourselin. “Fast free-form deformation using graphics processing units. Comput Methods Programs Biomed.” 2010 Jun;98(3):278-84.

Zhuang, X., Rhode, K., Razavi, R., Hawkes, D. J., Ourselin, S. “A Registration-Based Propagation Framework for Automatic Whole Heart Segmentation of Cardiac MRI.” IEEE Transactions on Medical Imaging. 2010

Translational Imaging Platform: Our team is leading the translational imaging research program between CMIC and the Institute of Neurology, and has established in collaboration with Prof Nick Fox a new imaging unit at Queen Square to deliver novel imaging biomarkers for clinical trials. Our Grand Challenge is to develop the fundamental building blocks enabling research at IoN - and beyond after first proof of concept - to take full advantage of a common software environment platform, facilitating the development and rapid deployment of complex and robust pipelines that have applications across diseases and research questions (M. Clarkson, A. Duttaroy).

NifTK: as part of our effort to translate image analysis tools to the clinic, we are also strongly involved into fast image processing using GPU and support of the open source community. Our team has been developing a set of self contain software packages for image registration (NiftyReg), segmentation (NiftySeg), reconstruction (NiftyRec), but also for biomechanical modeling (NiftySim). All these packages are downloadable from the CMIC website and available under SourceForge.

Alzheimer’s Disease: Imaging biomarkers now play a key role in both basic and applied research and are increasingly seen as important in diagnosis, drug discovery and therapeutic trials. In collaboration with

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using genetically modified mice. Our research effort is this large and multidisciplinary project is to provide image analysis capability for high-throughput high-resolution imaging (B. Sinclair, M. Modat, J. Cardoso).

Cardiac Imaging: we are working with KCL (R. Rezavi, G. Penney), Imperial College (D. Rueckert), and GOSH (A. Taylor) to develop fully automatic 3D and 4D anatomical models of the heart. Specifically, our main research is the development of registration and segmentation algorithms (X. Zhuang) using MR and Ultrasound images.

Neonetal Imaging: Very premature children are at risk of learning difficulties in childhood that are underpinned by slow processing of information and poor everyday memory. We wish to find out how measureable these are in infancy and whether they relate to brain growth after birth, In collaboration with the UCL Institute for Women’s Health (N. Marlow, N. Roberston) we are developing novel robust MR biomarkers to speed up clinical translation of neuroprotective agents which will have a positive benefit to the neurocognitive outcome of the premature children (A. Melbourne).

Grants Imaging to Assess Efficacy and Safety of New Treatments for Alzheimer’s Disease

Technology Strategy Board TP/M1638A01/10/2007 to 30/09/2010 £439,667

Optimising Reconstruction to Accommodate Complex System Models for Spect

EPSRC G026483/127/10/2008 to 13/04/2012 £332,362

Grand Challenges: Translating Biomedical Modelling into the Heart of the Clinic

EPSRC EP/H02025X/101/10/2009 to 31/12/2013 £323,323

Enhanced Translation from Bench to Bedside Using Computational Imaging

National Institute for Health Research P1835301/05/2010 to 30/04/2013 £551,094

Improving measurement of disease progression and response to treatment in dementia using multi-modal images

EPSRC , Industrial Case 0900022X 01/10/2010 to 30/09/2013 £65,294

Prospective study of familial Alzheimer’s disease –quantification of regional brain atrophy, and characterisation of local atrophy patterns

Unnamed Charity01/09/2010 to 31/08/2013 £342,908

Do early MRI markers of brain growth predict infancy and childhood outcomes in very preterm babies?

SPARKS 01/02/2011 to 31/01/2013 £177,925

Post-graduate students•Abigail Andrews •Jorge Cardoso •Pankaj Daga •Albert Hoang Duc •Marc Modat •Stefano Pedomonte •Benjamin Sinclair •Jonathan Young

Research Associates: •Dr. Matthew Clarkson •Dr. Daniil Kazantsev •Dr. Shiva Keihaninejad •Dr. Kelvin Leung •Dr. Andrew Melbourne •Mr. Xiahai Zhuang

Sebastien Ourselin

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Software Engineer:•Mr Anind Duttaroy

Non-invasive Histology and Brain-Connectivity Mapping

Daniel Alexander leads the Microstructure Imaging Group http://cmic.cs.ucl.ac.uk/mig/. The group specializes in model- based imaging techniques to map microscopic features of tissue, such as cell size or packing density, over whole organs or organisms. The two key applications are non-invasive histology and brain connectivity mapping.

Non-invasive histology aims to replace classical histology, which cuts up a tissue sample and views it under a microscope, with non-invasive imaging techniques. Histology provides the gold-standard diagnosis of a wide range of diseases, but requires extraction of tissue from the patient typically through a biopsy. Non-invasive imaging techniques, such as magnetic resonance imaging (MRI), are sensitive to changes in microscopic features much smaller than the size of the image pixels. By modeling the effect of these features on the signal, we can estimate the features themselves. The first figure shows an example in which we estimate the average size of axon fibres in white matter in each image voxel. A single voxel can contain up to a million fibres, but we can detect differences in signal that depend on the distribution of fibre diameters.

Brain connectivity mapping aims to determine the wiring system of the brain: which regions are connected to one another and what are the routes of connection. Different kinds of imaging tell us about connectivity in different ways. Functional imaging techniques can highlight regions that are active during particular cognitive tasks. They provide evidence for connectivity when regions consistently appear active at the same times. Structural imaging techniques can provide estimates of fibre orientations in white matter, which we can follow through images to recover the trajectory of a connecting fibre pathway. The second figure illustrates estimates of fibre orientations in each voxel from various techniques, which support connectivity mapping.

The MIG group has particular expertise in an imaging technique called diffusion MRI, which we make heavy use of in both applications above. The technique measures

the dispersion pattern of particles, usually water molecules, within tissue. Tissue microstructure, such as cell walls and membranes, impede the mobility of water and thus determine the dispersion pattern. From measurements of the dispersion pattern, we can infer features of the tissue microstructure like cell size, shape and packing density or fibre orientation.

Although diffusion MRI is a key technique in our efforts, many other non-invasive imaging techniques also contribute, such as other MRI modalities, computed tomography (CT), positron emission tomography (PET) and microscopy.

Daniel Alexander

Selected publications

D.C. Alexander, P.L. Hubbard, M.G. Hall, L.A. Moore, M. Ptito, G.J.M. Parker and T.D. Dyrby Orientationally invariant indices of axon diameter and density from diffusion MRI NeuroImage, 52(4), 1374-1389, 2010.

Alexander,D.C. (2008). A general framework for experiment design in diffusion MRI and its application in measuring direct tissue-microstructure features. Magnetic Resonance in Medicine 60, 439-448. ISSN: 0740-3194

Seunarine,K.K.,Alexander,D.C. (2009) Multiple fibres: beyond the diffusion tensor. in Diffusion MRI (eds by Behrens,T.E.B.,Johansen-Berg,H., Elsevier.

Average axon diameter map (colour overlay) on a fractional anisotropy map (grey scale) from diffusion MRI.

Various estimates of the distribution of fibre orientations in each voxel of a human brain image from diffusion MRI.

Orientationally invariant axon diameter index

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Grants

A Monte-Carlo Diffusion Simulation Framework For Diffusion MRI

EPSRC EP/E064280/110/05/2007 to 27/11/2010 £389,662

Direct Measurements Of Microstructure From MRI

EPSRC G007748/112/08/2008 to 30/09/2013 £1,368,757

Connect - Consortium Of Neuroimagers For The Non-invasive Exploration Of Brain Connectivity And Tractography

EU European Commission FP701/12/2009 to 30/11/2011 €2M

Monte Carlo Random Effects Modelling in Diffusion MRI; a New Window on Micro structure and White Matter

EPSRC EP/G025452/0101/10/2008 to 30/09/2011 £342,895

Post-graduate students•Gemma Morgan •Eleftheria Panagiotaki •Matt Rowe •Torben Schneider •Kiran Seunarine

Research Associates: •Dr. Matt Hall •Dr. Ivana Drobnjak •Mr. Hubert Fonteijn •Dr. Bernard Siow •Dr. Gary Hui Zhang

Daniel Alexander

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Inverse Problems in Medical & Bio-medical Image Reconstruction

My research is primarily in tomography for medical imaging, specifically the application of inverse problem techniques to image reconstruction. Inverse Problems can be linear or non-linear, and either well posed or ill-posed. Ill-posed inverse problems usually require regularisation techniques which can be placed within the general framework of Bayesian estimation, where the assumed prior distribution of the image under consideration plays the role of a penalty term in a constrained or unconstrained posterior probability optimisation.

A topic of research for the last 20 years has been the development of optical tomography an imaging modality detecting the contrast of absorption and scattering of light in the visible and near-infrared region of the spectrum. In this wavelength range photons are so strongly scattered that they are quite well described by a diffusion or randomwalk process in which the density of photons follows a Gaussian distribution with respect to distance from a source. The inverse problem for this imaging modality is non-linear and ill-posed. Significant improvements in image quality can be gained by using time-of-flight measurements using photoncounting detectors. This technique has been pioneered in the Medical Physics department which is one of the co-hosts of CMIC.

Optical tomography extends to fluorescence optical tomography in which the contrast is the stimulated emission of light at another wavelength, discriminated from the background by spectral filtering, and photoacoustic tomography where the contrast is stimulated emission of ultrasound waves, which do not suffer significantly from attenuation.

Other research topics that use similar methods are fast cardiac MRI, digital tomosynthesis, electro cardio physiology, and nuclear medicine (SPECT and PET). As well as the use of generic priors such as distribution of edges, I am currently investigating the use of cross-modality priors

which involves other topics such as information-theoretic measures, probabilistic atlases, and image registration.

Simon Arridge


Lehtikangas O, Tarvainen T, Kolehmainen V, Pulkkinen A, Arridge SR, Kaipio JP. Finite element approximation of the Fokker-Planck equation for diffuse optical tomography J Quant Spectroscopy, 111, 10, 2010.

Rudge T, Soloviev V, Arridge A. Fast Image Reconstruction in Fluoresence Optical Tomography Using Data Compression, Optical Letters, 35, 5, 2010.

Schweiger M, Dorn O, Zacharopoulos A, Nissila I, Arridge S. 3D Level Set Reconstruction of Model and Experimental Data in Diffuse Optical Tomography. Optics Express, 18, 1, 2010.

S. R. Arridge, J. C. Schotland, (2009), “Optical Tomography: forward and inverse problems”, Inverse Problems , 25(12), 123010 (59pp)

A. Bassi, C. D’Andrea, G. Valentini, R. Cubeddu, S.R. Arridge, (2009), “Detection of inhomogeneities in diffusive media using spatially modulated light”, Optics Letters, 34, 2156-2158. Optical tomographic reconstruction

showing the effect of compression.

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Simon Arridge

Grants

Parameter And Structure Indentification In Optical Tomography

EPSRC EP/E034950/101/06/2007 to 30/04/2011 £594,175

Small Animal Imaging By Fluorescence Lifetime Tomography

Wellcome Foundation COLL- 086114/Z/0B/Z01/12/2008 to 30/11/2011 £174,563

Fmtxct - Hybrid Fluorescence Molecular Tomography and X-Ray Computed Tomography

System and Method

EU European Commission FP7 20179201/03/2008 to 29/02/2012 €330,106

Neuropt: Non invasive imaging of brain function & disease by pulsed near infra-red light

EU European Commission FP7 20107601/04/2008 to 31/03/2012 €291,964

Post-graduate students•Josias Elisee •Jean Remond •Guang Yang

Research Associates: •Dr. Marta Betcke •Dr. Teresa Correia •Dr. Martin Schweiger •Dr. Vadim Soloviev •Dr Surya Prerapa • Dr Tanja Tarvainen

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Magnetic Resonance Imaging

My research focuses on obtaining clinically useful information from Magnetic Resonance Imaging. Theapproaches encompass both the data acquisition and reconstruction of images.

David Atkinson

Magnetic Resonance Imaging (MRI) provides detailed patient images in any 3D orientation and without the use of ionising radiation. Images can show anatomy or provide information on processes such as the velocity of flowing blood, contractility of the heart, tumour characteristics and, through diffusion weighted imaging, the cellular environment. Motion caused by respiration or the cardiac cycle is a problem for many of these techniques. Motion during a single scan can lead to image blurring and ghosting, whilst motion between individual scans makes comparison of images difficult.

Our group has shown mathematically how to motion-correct individual MR images [Batchelor] provided the motion is known. Recently we have used approaches such as training data [White] and information from the large number of receiver coils available on new systems [Odille] to provide motion correction information and generate improved quality images.

Motion misalignment of separate images requires registration methods and we developed a technique robust to both motion and image intensity changes for liver cancer imaging [Melbourne].

Undersampling the MR data leads to faster acquisition and has enabled us to make real-time blood flow measurements during free breathing and exercise [Steeden]. Undersampling also makes the image reconstruction more complex and time consuming but we have exploited the parallel processing of modern graphics cards to reduce reconstruction times [Sorensen].

Collaborations include the UCL Institute of Child Health, UCL Hospital, King’s College London, Imperial College London and The Institute for Cancer Research.


Batchelor PG, Atkinson D, Irarrazaval P, Hill DLG, Hajnal JV, Larkman DJ. Matrix description of general motion correction applied to multishot images. Magnetic Resonance in Medicine. Vol.54 p.1273-1280 (2005).

Melbourne A, Atkinson D, WhiteMJ, Collins M, Leach M, Hawkes D. Registration of dynamic contrast enhanced MRI using a progressive principal component registration (PPCR). Physics in Medicine and Biology. Vol.52 p.5147-5156 (2007).

Odille F, Uribe S, Batchelor PG, Prieto C, Schaeffter T, Atkinson D. Model-based reconstruction for cardiac cine MRI without ECG or breath holding. Magnetic Resonance in Medicine. Vol.63 p.1247-1257 (2010).

Sorensen TS, Atkinson D, Schaeffter T, Hansen MS. Real-Time Reconstruction of Sensitivity Encoded Radial Magnetic Resonance Imaging Using a Graphics Processing Unit. IEEE Transactions on Medical Imaging. Vol.28 p.1974-1985 (2009).

Steeden, J. A., Atkinson, D., Taylor, A. M., Muthurangu, V. (2010). Assessing vascular response to exercise using a combination of real-time spiral phase contrast MR and noninvasive blood pressure measurements. JOURNAL OF MAGNETIC RESONANCE IMAGING 31(4), 997-1003

White MJ, Hawkes DJ, Melbourne A, Collins DJ, Coolens C, Hawkins M, Leach MO, Atkinson D. Motion artefact correction in free-breathing abdominal MRI using overlapping partial samples to recover image deformations. Magnetic Resonance in Medicine. Vol.62 p.440-449 (2009).

Speech imaging reconstructed at the same rate as the acquisition using graphics cards. Two frames from the non-Cartesian kt-SENSE acquisition (Top and middle) and the temporal profile (right). The volunteer is sounding out “aba ada aka”.

Sorensen et al. IEEE Trans Med Imag 28 p1974-1985 (2009).

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Grants

Clinical Information from the MR Imaging of Moving Structures

National Institute for Health Research10/08/2010 to 26-09/2014 £41882

Towards Reliable Diffusion on MRI of Moving Organs

EPSRC 01/01/2011 to 31/12/2014 £402,449

Post-graduate students:•Valentin Hamy •Grzegorz Kowalik •Jenny Steeden •Benjamin Tremoulheac

Research Associate: •Dr. Freddy Odille

David Atkinson

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My research is focused on the development of novel image registration techniques to enable accurate biopsy sampling and delivery of minimally-invasive therapies. I am particularly interested in procedures where three-dimensional (3D) ultrasound is a viable method for imaging during the procedure.

Dean Barratt

There are a large number of diagnostic and therapeutic surgical procedures for which interventional imaging alone is suboptimal for the purposes of image guidance. For example, ultrasound imaging provides a safe, inexpensive, and convenient method for localising anatomical structures during a wide range of procedures, but typically provides limited information on the location and extent of disease and has a restricted field-of-view, compared with the detailed anatomical and pathological information represented by magnetic resonance (MR) and x-ray computer tomography (CT) images that are commonly used for diagnosis or surgical planning.

Image registration provides a method for fusing information from high-quality images, acquired prior to a procedure, with interventional images, overcoming the limitations of just using interventional imaging for surgical guidance. In particular, making high-quality anatomical and pathological information directly available during a procedure potentially leads to much greater accuracy than can be achieved using interventional imaging alone.

The overall aim of my research is to develop practical methods for registering pre-procedural images to interventional images, particularly, ultrasound images. Key challenges addressed by the research include registering images with very different pixel/voxel intensity characteristics, compensating for soft-tissue motion and deformation, and the development of automated methods that involve very little user interaction to register images in the clinical setting.

At present, I am principally interested in registering ultrasound images, acquired during needle biopsy and minimally-invasive surgical interventions for prostate and hepatobiliary cancers, to pre-procedure MR and CT images. Other applications of interest include


Hu Y, Ahmed HU, Taylor Z, et al. (2010) MR to ultrasound registration for image-guided prostate interventions. Med Image Anal (In Press).

Barratt DC, Chan CSK, Edwards PJ, et al. (2008) Instantiation and registration of statistical shape models of the femur and pelvis using 3D ultrasound imaging. Med Image Anal 12: 358-374.

Barratt DC, Penney GP, Chan CSK, et al. (2006) Self-calibrating 3D-ultrasound-based bone registration for minimally invasive orthopedic surgery. IEEE Trans Med Imaging 25(3): 312-323.

Barratt DC, Ariff BB, Humphries KN, et al. (2004) Reconstruction and quantification of the carotid artery bifurcation from 3-D ultrasound images. IEEE Trans Med Imaging 23(5): 567-583.

Barratt DC, Davies AH, Hughes AD, et al. (2001) Optimisation and evaluation of an electromagnetic tracking device for high accuracy three-dimensional ultrasound imaging of the carotid arteries. Ultrasound Med Biol 27: 957-968.

neuro and orthopaedic surgery, where ultra-sound is not yet used routinely, but potentially provides a useful non-invasive method for localising anatomical structures.

In terms of methodology, my current research utilises computational techniques for modelling variations in organ shape, soft-tissue deformation, and ultrasound image formation; techniques for extracting features, such as blood vessels, that can be used as a basis for image registration algorithms; and position tracking devices for freehand 3D ultrasound acquisition.

(a) Transverse MR image of the prostate gland with the capsule and cancerous region overlaid.

(b) Corresponding ultrasound image following registration of the ultrasound and MR volumes. The arrows indicate small cysts, which provide a visual indication that the volumes are accurately aligned.

Image Registration for Guiding Surgical Procedures

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Grants

Ultrasound Image Registration For Guiding Medical Interventions Royal Academy of Engineering Research Fellowship R1762001/10/2006 to 30/09/2011 £452,097

Enabling Minimally-Invasive Interventions Using Multimodal Image Guidance National Institute for Health Research UCLH/UCL Comprehensive Bio-medical Research Centre (Project grant 96) 01/12/2008 to 30/11/2011

£191,212

Image Targeting For The Focal Treatment Of The Prostate National Institute for Health Research Invention fo Innovation (i4i) grant 11 3A 0809 10029 01/07/2010 to 31/03/2011 £87,528

Post-graduate students:•Daniel Heanes •Pierre Gélat

Research Associates: •Erik Jan-Rijkhorst•Yipeng Hu

Dean Baratt

15

CMIC Researchers(in alphabetical order)

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In-Vivo Microscopy & Relocalisation of Biopsy Sites in Gastroenterology

My research work focuses on new applications of in-vivo microscopy based around a fibred confocal micro-endoscope and in particular the tracking of the location of microscopic biopsy sites detected by optical biopsy in endoscopic images.

Baptiste Allain


Allain, B., Hu, M., Lovat, L.B., et al., (2009), Biopsy Site Re-localisation based on the Computation of Epipolar Lines from Two Previous Endoscopic Images, MICCAI 2009, 5761: 491–498.

Allain, B., Hu, M., Lovat, L.B., et al., (2010), A System for Biopsy Site Re-targeting with Uncertainty in Gastroenterology and Oropharyngeal Examinations, to be published in MICCAI 2010.

Epithelial cancers and endothelial diseases arise at the superficial layers of tissues and are characterised by microscopic features. These lesions are invisible macroscopically and may be detected by in vivo optical biopsy for example probe-based confocal laser endomicroscopy. Once detected, they need to be re-localised in their spatial context for treatment or for instrument guidance.

A particular clinical application of optical biopsy is the diagnosis of Barrett’s Oesophagus in Gastro-enterology. A probe is passed via the working channel of conventional endoscopes and placed in contact with the tissue surface to perform an optical biopsy. We developed a method to re-localise the biopsy site in the next endoscopic images once the probe has been removed. It makes use of the epipolar geometry formed between at least 2 images where the biopsy site location is known and a target image where it needs to be re-localised. The re-localisation result is displayed as a confidence region in the target image. The re-localisation accuracies were better than 0.8mm.

This work was done in collaboration with the Dental Institute, Department of Biomaterials, King’s College London,

Use of probe-based confocal laser endomicroscopy for the Diagnosis of Barrett’s Oesophagus (Image courtesy: Mauna Kea Technologies).

Biopsy site re-localisation principle based on the epipolar geometry formed between at least three endoscopic images.

Display of the re-localisation result in the target image: a confi-dence ellipse is drawn and indicates where the site is likely to be. A enlarged image can be displayed as well.

the Department of Gastroenterology, University College London Hospitals, and Mauna Kea Technologies. It is funded by the Department of Health, DLH Clinician Scientist Award to Dr. Cook, EPSRC (EP/C523016/1), Department of Health’s NIHR Comprehensive Biomedical Research Centre (University College London Hospitals), Cancer Research UK: grant to the UCL Experimental Cancer Medicine Centre. This work expresses the authors’ views and not necessarily the funders’.

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Inverse Problems and Image Reconstruction

I am interested in reconstruction and analysis of data from various measurement modalities like e.g. X-ray or Optical Tomography with applications ranging from medical to industrial or security imaging.

X-ray cone beam CT

The slow rate of the data acquisition in the widely clinically employed Helical Cone Beam CT is an essential handicap for deployment in time critical procedures like e.g. luggage screening at the airport. With the limiting factor being the mechanical motion of the gantry, recently multi-source scanners have been developed. In such systems sources can be electronically switched, thereby obviating the slow mechanical motion. During my postdoc at the University of Manchester, I worked on rebinning methods for reconstruction of data from such multisource scanners. I have continuous interest in development of reconstruction algorithms and measurement acquisition schemes for multi-source X-ray CT systems.

Tomosynthesis

Tomosythesis affords the opportunity for multiple views X-ray Imaging. The major difference to the regular CBCT is the limited number of views, which allows only for low depth resolution in the reconstructed image.

The cost of equipment can be a limiting factor for deployment of advanced techniques for luggage security screening. Therefore cost effective systems like the On-Belt-Tomosynthesis, which can be easily integrated with the existing structures, are of great interest.

Marta Betcke

Image from Rapiscan Systems Real Time Tomography Cone Beam Scanner reconstructed with Two-Sheet Rebinning Method.

Clinical applications of Tomosynthesis are in e.g. breast cancer screening, where Tomosynthesis can potentially replace the widely used mammography scanners.

I am interested in optimal design of such systems, as well as development of tailored methods for image reconstruction.

Optical Tomography

Optical Tomography is a non-invasive low resolution imaging modality, where one attempts to reconstruct optical properties of the tissue from measurements of light penetrating the tissue. The inverse problem is severely ill-posed, making data reconstruction a challenging task. In fact meaningful results can only be obtained under additional assumptions (priors). I am interested in analysis of, in particular, sparsity related priors and in development of optimal measurement acquisition schemes and efficient numerical methods for ill-posed image reconstruction under sparsity assumptions.

Image Reconstruction: the Sparse Way

I am currently holding an EPSRC Postdoctoral Fellowship with the goal to utilize sparsity to develop new acquisition and reconstruction schemes for faster and more robust imaging using substantially fewer measurements than necessary today.

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Brain Segmentation and Cortical Thickness Estimation

My research is focused on improving the accuracy of neuronavigation systems by estimation and compensation of brain shift that happens during a typical neurosurgical procedure. Improved neuronavigation systems can lead to better surgical outcome for patients by maximising resection of target areas whilst minimising damage to healthy tissue and critical brain structures during surgery.

Methods that measure the thickness of the cerebral cortex from in-vivo magnetic resonance (MR) images all rely on an accurate segmentation of the MR data. However, due to the presence of noise, intensity non-uniformity, partial volume effects, limited resolution of MRI and the highly convoluted shape of the cerebral cortex, segmenting the brain in a robust and accurate way still poses a challenge.

Building on existing EM based segmentation algorithms, one can change the behaviour of the segmentation algorithm is areas classified as sulci and gyri, allowing a robust segmentation throughout whilst maintaining all the cortical detail. Furthermore, by looking at the anatomy, one can set all the neighbouring rules in the MRF component of the

M.Jorge Cardoso

segmentation algorithm, resulting in an anatomically consistent layering of the segmented classes.

The cortical thickness is then computed using a Laplace equation based thickness model. The idea is to solve the Laplace equation considering each side of the cortex as an equipotential structure and then integrate the potential streamlines that connect the WM/GM and the pial surface.

Statistics are then calculated between populations in a voxel-by-voxel or vertex-by-vertex manner resulting in maps of statistical difference. The statistical cortical thickness differences between controls and AD patients can be seen below, where thinning and thickening of the cortex is represented by the red and green colour respectively.


Cardoso, M. J., Clarkson, M. J., Ridgway, G. R., Modat, M., Fox, N. C., Ourselin, S., Improved maximum a posteriori cortical segmentation by iterative relaxation of priors, MICCAI 2009

Cardoso, M. J., Clarkson, M. J., Modat, M., Ridgway, G. R., Ourselin, S., Locally weighted Markov Random Fields for cortical segmentation, ISBI 2010

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Towards Image-Targeted Focal Therapy of Prostate Cancer

Whole-gland treatments for prostate cancer, using radiation, cryotherapy, and other minimally-invasive tissue ablation techniques are associated with high rates of impotence, incontinence and rectal toxicity. Focal therapy, in which just the cancerous regions are treated, has the potential to substantially reduce these side- effects by avoiding damage to critical structures, such as nerves.

In order to treat focally it is necessary to identify cancerous regions of the prostate, and to confirm the absence of cancer from the rest of the gland. Recent research indicates that magnetic resonance (MR) imaging is sufficiently sensitive for this purpose. We have created tools to enable our clinical collaborators to perform quantitative MR image analysis, which form the basis of a computer-aided diagnosis system being developed.

It is not usually possible to detect prostate cancer on the ultrasound images that are used to guide treatments. We have developed software to align preoperatively acquired MR images with ultrasound images of the prostate acquired during treatment. We are currently integrating this registration technology into treatment devices so that the MR-detected cancer can be overlaid on the ultrasound image and then targeted.

We collaborate closely with clinicians within the UCL Divisions of Surgery (Professor Mark Emberton and colleagues) and Imaging (Dr Shonit Punwani and colleagues).

We are also working closely with UCL Business in order to translate this research into a commercially available product for image-targeted prostate biopsy and therapy via a UCL spin-out company (Sageta Ltd) and a close

Tim Carter


Hu et al, MR to Ultrasound Image Registration for Guiding Prostate Biopsy and Interventions, MICCAI 2009

Carter et al., Application of Soft Tissue Modelling to Image-Guided Surgery, Medical Imaging and Physics, 2005

I am a senior research associate in the Centre for Medical image Computing at UCL. My research involves developing techniques to improve the identification of cancer in magnetic resonance images (MRIs), and to enable accurate targeted ablation of prostate cancer using novel, minimally invasive therapy devices.

Example of an ultrasound image of the prostate used to target minimally invasive therapies, such as HIFU.

Multi-parametric MRIs are used to identify the location of prostate cancer. T2-weighted imaging (upper image) shows the structure of the prostate whilst the apparent diffusion coefficient (lower image) and contrast enhanced imaging allows improved cancer detection.

link USHIFU Lld, who manufacture a commercial system for high-intensity focused ultrasound (HIFU) treatment of prostate cancer.

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Biomarkers For Neuro-Degenerative Diseases

The human cerebral cortex is a highly folded layer or ribbon of interconnected neurons, with an average thickness of around 2.5mm - varying between 1 and 4.5mm in different parts of the brain. The cortex plays a key role in most cognitive processes and demonstrates regional specification such that visual functions, language, calculations, executive functions and so on have localised cortical representation in different parts of the brain. The thickness of the cortex is of interest as it develops, follows the normal ageing process and changes under a wide variety of neurodegenerative diseases. However, measuring the thickness of the cortex is a challenging task.

In conjunction with Jorge Cardoso, we have improved the segmentation of the cortex from magnetic resonance images using the Expectation Maximisation algorithm. In addition, we have been validating different voxel based methods to compare with the more established surface based methods. We find that voxel based methods are comparable to surface based methods such as FreeSurfer for group-wise studies (see Figure 1), and we continue to work on longitudinal developments of the voxel based methods. If validated, these voxel based methods may be many times quicker than the current surface based methods.

Once the thickness is measured, we need to understand how the thickness changes in different regions. Defining these regions may be done by using a reference atlas created from many different subjects. Most recently we began to investigate if diffusion based imaging can be used to improve the delineation of cortical regions, so that eventually the power to detect change will be increased (see Figure 2). By combining information from different images, we aim to improve the sensitivity of measures like cortical thickness.

Matt Clarkson


[1] Clarkson M. J., Malone I. B., Modat M., Leung K. K., Ryan N., Alexander D. C., Fox N. C., Ourselin S., (2010) A Framework For Using Diffusion Weighted Imaging To Improve Cortical Parcellation. MICCAI 2010.

[2] Cardoso M. J., Clarkson M. J., Ridgway G. R., Modat M., Fox N. C., Ourselin S., (2009) Improved maximum a posteriori cortical segmentation by iterative relaxation of priors, MICCAI 2009

[3] Clarkson M. J., Ourselin S., Nielsen C., Leung K. K., Barnes J., Whitwell J. L., Gunter J. L., Hill D. L. G., Weiner M. W., Jack Jr. C. R., Fox, N. C., (2009) Comparison of phantom and registration scaling corrections using the ADNI cohort. NeuroImage 2009

My research is focussed on developing more accurate and reliable measures of brain shape change over time, with a particular emphasis on neurodegenerative diseases including Alzheimer’s disease. These indicators of change, called biomarkers, are of particular interest in applications such as clinical drugs trials, and early disease diagnosis.

Fig. 1: A comparison of FreeSurfer (left), Laplacian (middle) and Registration (right) methods, showing areas of statistically significant thinning of the cortex in Semantic Dementia patients.

In addition, I am responsible for developing, along with Anind Duttaroy and Muhammad Adnan, a software platform for delivering CMICs medical image processing technology to clinical partners within UCL and beyond.

Fig. 1: A comparison of FreeSurfer (left), Laplacian (middle) and Registration (right) methods, showing areas of statistically significant thinning of the cortex in Semantic Dementia patients.

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FMT Inversion Using XCT Image Priors Without Strong Anatomy Function Correlations

Fluorescence molecular tomography (FMT) is a relatively new optical imaging modality that uses fluorescent markers that accumulate in specific regions, to monitor cellular and subcellular functional activity in-vivo in small animals. In FMT, near-infrared (NIR) light sources at excitation wavelength are directed to the animal body at different projection angles. The excitation light travels diffusely into the tissue and part of the photons are absorbed by fluophores, which re-emit part of the light at a longer wavelength. Then, both the excitation and fluorescence projected light intensities are measured using a charge-coupled device (CCD) camera placed opposite the source. These multiple projections are combined to obtain the distribution of fluophore concentration. However, due to the diffusive nature of light propagation in biological tissue the image reconstruction is an ill-posed inverse problem, and in addition, it is also underdetermined. Hence, in order to obtain a stable and meaningful solution one has to use regularisation methods.

Prior structural anatomical information from X-ray computer tomography (XCT) images can be incorporated into the reconstruction to improve optical imaging performance.

However, some image priors may lead to biased solutions. We aim to develop and analyse the performance of different priors, which improve the accuracy of the FMT problem without biasing the solution

Teresa Correia


T Correia, A Gibson, and J Hebden, Identification of the optimal wavelengths in optical topography: a photon measurement density function analysis, J. Biomed. Opt. (submitted) (2010)

T Correia, A Banga, N L Everdell, A P Gibson and J C Hebden, A quantitative assessment of the depth sensitivity of an optical topography system using a solid dynamic tissue-phantom, Phys. Med. Biol. 54 6277-6286 (2009)

J C Hebden, T Correia, I Khakoo, A P Gibson and N L Everdell, A dynamic optical imaging phantom based on an array of semiconductor diodes, Phys. Med. Biol. 53 N407-N413 (2008)

T Correia, A Gibson, M Schweiger, and J Hebden, Selection of regularization parameter for optical topography”, J. Biomed. Opt. 14, 034044 (2009).

My research interests are in the area of optical tomography. I completed my PhD in 2010 at UCL under the supervision of Jem Hebden. I am currently employed as a research associate working in the Centre of Medical Image Computing with Simon Arridge.

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Brain Shift Estimation

Intra-operative magnetic resonance (iMR) machines allow imaging of the patient during surgery. iMR is a useful tool for intermittent monitoring of the surgical progress. Coupled with a neuro navigation system, they can provide up-to-date information during surgery and are an important part of the surgical decision support system. The current state of the art neuro navigation systems are limited because of their inability to accurately estimate brain shift during the surgery. This is because they assume an affine relationship between the intra-operative and the pre-operative images. The deformations caused by the brain shift cannot be accurately determined using a simple parametric (rigid or affine) transformation. This limitation makes it difficult to rely on the pre-operative images for accurate identification of surgical targets and eloquent brain areas.

Non-rigid registration accommodates many more degrees of freedom and allows modelling of more realistic non-linear deformations like the brain shift. However, these registration algorithms are computationally expensive and have been largely considered impractical for use in real time clinical scenarios. In my first year, I have been heavily involved in the development of a GPU based image registration framework that is suitable for use in the neurosurgical context.

The software package is named

Pankaj Daga


Daga et al. Near real time brain shift estimation for interventional MRI suite, High performance Computing, MICCAI 2010.

Open source software:

NiftyReg: A software framework for performing fast non-rigid image registration using GPUs. http://sourceforge.net/projects/niftyreg

My research is focused on improving the accuracy of neuronavigation systems by estimation and compensation of brain shift that happens during a typical neurosurgical procedure. Improved neuronavigation systems can lead to better surgical outcome for patients by maximising resection of target areas whilst minimising damage to healthy tissue and critical brain structures during surgery.

NiftyReg and has been released to the medical imaging community as open source software.

The next stage of my research will focus on combining diffusion weighted images (DWI) along with anatomical T1 MR data to produce deformations that may be more accurate. Indeed, DWI provide rich information about the white matter structure whereas T1 MR images reflect the structure of brain grey matter. The complementary information from these to imaging modalities in the registration scheme could provide a more accurate and realistic deformation.

Tracking optic radiation during different phases of a surgery.

Intra-op image Affinely registered pre-op image

Checkerboard after affine registraton

Checkerboard after nonrigid registraton

Illustration of improved brain shift estimation using non-rigid registration

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Drobnjak I, Siow B, Alexander D.C. (2010) Optimizing gradient waveforms for microstructure sensitivity in diffusion weighted MR. JMR 206 (41-51)

Drobnjak I, Siow B, Alexander D. (2010) Improving axon-diameter measurements by direct diffusion gradient waveform optimization. Human Brain Mapping.

Drobnjak I, Siow B, Alexander D. (2010) Optimal diffusion gradient waveforms for measuring axon diameter. ISMRM

Optimising gradient waveforms

Diffusion weighted MR is used in bio-medical imaging where it offers the potential to map microstructural features (such as axon radius) in tissue. Imaging axon radius is a key challenge as a reliable technique could provide insight into neuronal diseases that alter axon radius distribution, such as autism, Alzheimer’s disease or schizophrenia.

Techniques for imaging axon radius that use standard diffusion protocols with rectangular gradient waveforms (such as PGSE sequence) rely on strong gradient strengths and long acquisition times, which makes them impractical for human in-vivo studies since human imaging systems have limited gradient strengths of up to around 0.08T/m.

My research focuses on optimizing diffusion protocols by optimizing the shape of the gradient waveform to give the best estimate of axon radius, and also to be achievable on live human subjects and patients.

The gradient waveform is defined discretely and each point optimized

Ivana Drobnjak

My research focuses on finding the best diffusion MR imaging protocols that can measure the microstructure parameters of the brain, such as the size of axon radius in white matter.

independently. The generalized pulse sequence is named GEN and can be seen in Fig 2. Simulation experiments find that the identified class of optimized gradient waveforms have dominant square wave components with frequency that increases as the radius size a decreases (Fig2). Optimized protocols produce identifiable axon radius estimates (Fig3) for human scanner values, of higher precision and accuracy than using the standard rectangular waveform protocols.

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Fig1 Axon bundles in white matter tissue of the human brain

Fig3 Estimates of the action radius using optimized gradiant waveformsFig2 GEN pulse sequence

and optimized generalized gradient waveforms

Innovative Numerical Methods for Diffuse Optical Imaging

Diffuse Optical Imaging (DOI), the study of the propagation of Near Infra-Red (NIR) light in biological media, is an emerging method answering the current concerns in medical imaging. Its state-of-the-art is non-invasive, versatile and reasonably expensive, allowing new investigations to be carried out. The DOI physical problem is here treated with the Diffusion Approximation (DA) scheme and various reconstruction techniques. In the particular framework of Diffuse Optical Tomography (DOT), the development of numerical methods such as the Finite Element Method (FEM) and more recently the Boundary Element Method (BEM), has allowed the treatment of complex problems, even in vivo functional three-dimensional imaging. However, these two methods have always been separated and this work is the first attempt to combine them. Though not new in numerical simulations and problem solving, this combination of the Boundary Elements Method (BEM) and the Finite Elements Method (FEM) needed a theoretical study in our domain. The BEM-FEM is designed to tackle layered turbid media problems. It focuses on the region of interest by creating a volume mesh and reconstructing in this region only.

All other regions are treated as

Josias Elisee


Combination of Boundary Element Method and Finite Element Method in Diffuse Optical Tomography

By J. Elisee, A. Gibson, S. Arridge, IEEE Transactions on Biomedical Engineering, 2010, in press

I am a PhD student with Prof. S. Arridge and Dr. A. Gibson at the University College London, Department of Computer Science and Department of Medical Physics. My background is in Applied Physics, a field in which I graduated from at the Ecole Centrale Paris and the Paris VI University. My current interests are in computational physics modelling, applied to optical tomography, fluorescence molecular tomography and related medical imaging technology. These include segmentation and registration techniques.

piecewise-constant in a surface-integral approach. We validate the model in concentric spheres, with different positions of the volume-integral treated area and found it compared well with an analytical result. We then performed functional imaging of the neonate’s motor cortex in vivo, in a reconstruction restricted to the brain, both with FEM and BEM-FEM. These results show the effectiveness of the BEM- FEM in situations where the organ of interest is surrounded by superficial layers.

In the interest of computational speed, we are developing an acceleration technique for the BEM part of the code. Such a method could ease the evaluation of the BEM matrix, along with a faster obtainment of the solutions.

Another use of the BEM in Diffuse Optical Imaging (DOI) is also outlined. NIR Spectroscopy (NIRS) is nowadays a common sight in any research group equipment which deals with blood-related issues. It is particularly used in brain monitoring. Unfortunately, this technique is very often accompanied by rudimentary analysis of the data and the three-dimensional appreciation of the problem is missed. The innovative method we are developing represents a step forward.

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Anatomical and Functional Connectivity of the Human Brain

The human brain is organized into a set of regions that are at the same time highly specialized for very specific tasks and in constant communication with each other to enable our everyday behaviour. My research focuses on both aspects of brain organization. I am especially interested in how measures that quantify specialization and integration can be used clinically.

In the first project I am investigating the use of resting-state fMRI data and clustering methods to establish a map of specialized regions in the brain. It has been known now for more than a decade that brain regions keep communicating to each other, even when the subject does not engage in an overt task. The identification of so-called resting-state networks is by now a routine procedure with a high level of re-producibility and has found its use in for instance studies of Alzheimer’s disease. However, a further subdivision of these networks into specialized regions would allow a much more specific analysis of resting-state connectivity. I am using model-based clustering methods to develop a map of resting-state activity on the regional level. I am using model-selection techniques to establish the appropriate level of subdivision.

In the second project, I am investigating how measures of anatomical connectivity can be used to plan Convection Enhanced Delivery interventions. The delivery of drugs to the brain is notoriously difficult, because drugs often are not able to pass the Blood-Brain-Barrier In Convection Enhanced Delivery, the drugs are directly injected into the brain, which circumvents this obstacle. The distribution of drugs after injection is intimately related to the microstructure of the local tissue. I am using Diffusion MRI, which is informative about the brain’s

Hubert Fonteijn

I’m a post-doc working in Daniel Alexander’s Microstructure Imaging Group. I’m an applied physicist with a specific interest in brain connectivity measures as measured by MRI. My PhD project was a joint project with Frans Verstraten at the department of Psychology in Utrecht and David Norris at the Donders Institute for Brain, Cognition and Behaviour in Nijmegen, both in the Netherlands. In my PhD I have investigated MRI measures of anatomical and functional connectivity and their integration. I am currently interested in segmentation methods, based on resting-state fMRI data and on models that relate disease progression to MRI measures.


Slice-based clustering of resting-state fMRI data, Fonteijn HM and Norris DG. International Society for Magnetic Resonance in Medicine, Honolulu, April 2009

The extracellular diffusion weighted signal predicts axon diameter distribution parameters, Fonteijn HM, Hall MG and Alexander DC. International Society for Magnetic Resonance in Medicine, Stockholm, May 2010-08-17

Determining infusion sites for Convection Enhanced Delivery using probabilistic tractography. Fonteijn HM, Woodhouse M, White E, Gill SS and Alexander DC. CDMRI workshop, MICCAI, Beijing, September 2010

microstructure, to predict drug distributions. I am moreover developing algorithms to determine the most optimal injection site, given the site that the drugs should target.

In the third and most recent project, I have been developing a novel model for disease progression in neuro-degenerative diseases. So far, determining whether a patient has Alzheimer’s Disease and in which stage the disease is in has only been possible post mortem. We have used structural MRI data, which can be readily acquired in vivo, to construct a detailed model of disease progression and we have shown good agreement with the gold standard post mortem model. In the future we will use this model to perform staging of patients in vivo.

Functional segmentation of the brain, based on resting-state fMRI data. The top image shows a typical resting-state network. The bottom image shows the complete segementation of the same slices into 20 clusters.

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Simulations of Biological Systems Being Measured with Diffusion MRI, and of the Scanner Doing the Imaging

Matt Hall

I’m a post-doc working as part of the Camino diffusion imaging toolkit project. I’m a mathematical physicist with a background in statistical mechanics. I took my PhD at Imperial College, in the Maths department’s Mathematical Physics section. I am interested in validation of diffusion MR reconstruction techniques, and a large part of my work has been to construct a highly detailed and sophisticated monte carlo simulation of diffusion in the brain.

Diffusion MRI analyses have come on leaps and bounds over the last 10 years or so, but how can you be sure that what you’re seeing is a really good indication of what’s actually there? And how can you make your scan as sensitive as possible to something you are interested in?

One way to check how accurate your images are, or to maximise sensitivity, is to use computer simulation. That way you know exactly what you’re looking at and you can check the results of your analysis against what’s in the simulation. Since diffusion MRI measures particles diffusing around in complex restricting structures, that’s what I’ve built a simulation of. We can simulate almost any structure: from simple cylinders to complex meshes constructed from stacks of microscope images so that the tissue can be of almost any shape and structure, and can simulate almost any diffusion pulse-sequence over the top to make highly accurate synthetic data.

The simulation has been used in-house to study the effects of tissue swelling, pulse-sequence optimisation and to test new measurement techniques that can get at new tissue properties, like axon radius.

It’s also used internationally by groups interested in testing their own pulse sequences and to test accuracy of their own image analysis methods. This is the most sophisticated system of its kind in the world and is under continuous development to add new physics and greater flexibility. It’s written in Java, and is available for free as part of the Camino diffusion MRI toolkit.


Hall,M.G., Alexander,D.C. (2009). Convergence and Parameter Choice for Monte-CarloSimulations of Diffusion MRI. IEEE Transactions on Medical Imaging 28(9), 1354-1364. ISSN: 0278-0062

Hall MH, Barrick TR, 2008. From diffusion-weighted MRI to anomalous diffusion imaging, Magnetic Resonance in Medicine 59 (3): 447-455.

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CT Guided Colonoscopy

Tom Hampshire

My PhD research involves registration of optical endoscopy video to CT colonograpy for preoperative CT guided colonoscopy

Colorectal cancer is a leading cause of cancer related mortalities, leading to over 639,000 deaths each year worldwide. Computed tomography colonography (CTC) is becoming established as a screening tool for detection and diagnosis of the disease, providing a non-invasive method for examination of the colon from CT volume data. To improve screening, computer aided detection (CAD) is being developed for the detection of colonic polyps, precursors to colorectal cancer.

There are some important limitations in CTC. Imperfect cleansing and air-insufflation can lead to portions of the colon wall to be collapsed, or covered with water. Furthermore, remaining faecal matter or folds of the colonic wall may mimic the appearance of colonic polyps, creating false positives in CAD methods. Routinely, the radiologist compares image sets from CT scans with the patient in both the prone and supine positions to assess movement of faecal matter and to examine areas of the colon hidden in the other view. A method for automatic registration of data from the prone and supine sets has the potential to be both time efficient, and provide a more accurate diagnosis under clinic evaluation or CAD.

Following a CTC screening, in the case when a patient has had a polyp identified, the treatment plan may involve undergoing a colonoscopy to perform an optical biopsy and remove the lesion. However, the success of colonoscopy is dependent on the physician’s skill and experience as it is a difficult task to navigate through the colon to the desired location. Identifying the correspondence between CTC and optical colonoscopy would allow visualisation of corresponding patient anatomy to guide the physician to the regions of interest identified in CTC.

A ‘virtual colonoscopy’ (top) reconstructed from a CT volume, and a frame from an optical colonoscopy

Collaborators

Supervisors: David Hawkes (UCL), Mingxing Hu (UCL)

Clinical Collaborators: Steve Halligan (UCLH), Brian Saunders (St. Mark’s Hospital), Darren Boone (UCLH)

Industrial Collaborators: Medicsight PLC

A surface mesh of the colon constructed from a pre operative CT colonography

Biomechanical Modelling of the Breast for Image Registration and Surgical Guidance

Lianghao Han

I am a postdoctoral research associate. I received the PhD degree from the University of Cambridge in 2005. My research interests include finite element methods, inverse problems, biomechanical modelling of soft tissues and multimodality image registration. I am the author of more than 40 referred conference and journal papers

A number of different imaging methods (such as MRI, X-Ray, Tomosynthesis and Ultrasound) are available in breast screening, biopsy diagnosis and surgery. Each method has its own strengths and weaknesses in the early detection and diagnosis of suspicious lesions; combing methods can provide more complete pathological information to improve breast cancer diagnosis. However, the shape of the breast and the position of suspicious lesions demonstrate differences in various medical images because the breast deformations are different due to different body positions, constraints and external forces applied to it during imaging procedures. To quantitatively compare medical images from different imaging procedures, the displacement field between two different images is required. The current research is focused on physically realistic modeling of large deformation of the breast, aiming to predict the changes of the shape and internal structure of the breast during imaging procedures and surgery. Patient specific biomechanical models of the breast are constructed using medical image data (MRI), and Finite element Methods (FEMs) are employed to simulate the large deformation of the breast with the combined effects of material and geometric nonlinearity. For fast or near real time clinical applications, a Total Lagrangian explicit dynamical FEM code was implemented on the graphics processing unit (GPU), and integrated with a material parameter optimisation process for breast tissues. The developed method has being used for image registration and surgical guidance.

Image registration result example. Difference images from different registration methods (a) no registration (b) Af-fine+free-form deformation (FFD) registration method (c) hybrid registration method (FEM-based registration + FFD).


1.Han, L., J. Hipwell, et al. (2010). Fast Deformation Simulation of Breasts Using GPU-Based Dynamic Explicit Finite Element Method. Digital Mammography. J. Martí, A. Oliver, J. Freixenet and R. Martí, Springer Berlin / Heidelberg. 6136: 728-735.

2.Laity, P. R., L. Han, et al. (2010). “Variations in compaction behaviour for tablets of different size and shape, revealed by small-angle X-ray scattering.” Journal of Pharmaceutical Science 99(10): 4380-4389.

3.Han, L, J. Elliott, et al. (2008). “Numerical simulation on pharmaceutical powder compaction.” Physical and Numerical Simulation of Materials Processing, 575-578: 560-565.

4.Han, L, J. A. Elliott, et al. (2008). “A modified Drucker-Prager Cap model for die compaction simulation of pharmaceutical powders.” International Journal of Solids and Structures 45(10): 3088-3106.

5.Han, L, J. A. Noble, et al. (2003). “A novel ultrasound indentation system for measuring biomechanical properties of in vivo soft tissue.” Ultrasound in Medicine and Biology 29(6): 813-823.

Patient specific FE model for large deformation simulation of breast compressed by two parallel plates during X-Ray mammography a) undeformed breast (b) compressed breast

(a)

(b)

(c)

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Ultrasound Therapy Guidance

Daniel Heanes

In my PhD work I am investigating algorithms to utilize diagnostic ultrasound data in the planning and navigation of a challenging new therapeutic application aimed at liver tumours.

High-intensity focused ultrasound (HIFU) is an emerging non-invasive therapy for the treatment of liver tumours.

As a non-invasive procedure, HIFU relies on the use of anatomical images for its targeting and guidance. However, breathing motion and any overlying ribs add special challenges to planning and navigation in-theatre which must be met to achieve its full potential for the liver. Additionally, accurate, patient-specific knowledge of the acoustic properties of overlying tissues will be required at the planning stage of a treatment.

Rapid and reliable methods for alignment of ultrasound and magnetic resonance images, are needed. My work has focused on assessing essential performance characteristics of competing and novel multimodal registration techniques relevant to the application. I am also investigating the feasibility of using raw echo data from diagnostic US transducers to infer acoustic parameters of tissue compartments via a nonlinear physical modeling approach.

I work on the Transcostal HIFU project in collaboration with physical modelling teams from UCL Mechanical Engineering and Oxford University, under financial support from the EPSRC. Clinical guidance and hardware expertise for the project are based primarily at the Institute of Cancer Research.

A FE mesh of the prostate (shown in red), built from a pre-operative MR image, is used to simulate the deformation that may occur during a surgical procedure. The TRUS probe mesh is shown in blue


Hu et al, Deformable Vessel-Based Registration Using Landmark-Guided Coherent Point Drift. MIAR 2010

Hu et al, Comparison between Transperineal and Transrectal Biopsy for the Detection of Prostate Cancer to Guide Focal Therapy. EAU 2010

Hu et al, MR to Ultrasound Image Registration for Guiding Prostate Biopsy and Interventions. MICCAI 2009

Hu et al, Modelling Prostate Gland Motion for Image-guided Interventions. ISBMS 2008

Hu et al, A Statistical Motion Model Based on Biomechanical Simulations for Data Fusion during Image-Guided Prostate Interventions. MICCAI 2008

Non Invasive Imaging of Brain Function and Disease by Pulsed Near Infrared Light

Juha Heiskala

I am research associate at UCL Dept. of Computer Science, working with image reconstruction in diffuse optical tomography. My research interests include use of prior information in analysis of optical imaging data, combining optical imaging data with information from other imaging modalities and brain hemodynamics.

I am working on the European nEUROPt project. The aim of the project is the development and clinical validation of use of diffuse optical imaging and related technologies for diagnosing and monitoring neurological diseases such as stroke, epilepsy, and cerebral hemorrhage and ischemia, based on diffuse optical imaging by pulsed near infrared light.

Established diagnostic imaging modalities (e.g. X-ray Computed Tomography, Magnetic Resonance Imaging, Positron Emission Tomography) provide 3D anatomical, functional or pathological information with spatial resolution in the millimetre range. However, these methods cannot be applied continuously or at the bedside. Diffuse optical imaging is expected to provide a valuable complementing tool to assess perfusion and blood oxygenation in brain tissue and their time evolution in a continuous or quasi-continuous manner.

The role of UCL Dept. of Computer Science in the project is to provide tools for interpreting optical data and reconstructing images that can be used for diagnostic and monitoring purposes.


J. Heiskala, M. Pollari, M. Metsäranta, P. E. Grant, and I. Nissilä, Probabilistic atlas can improve reconstruction from optical imaging of the neonatal brain, Optics Express 17, 14977-14992 (2009)

J. Heiskala, P. Hiltunen, I. Nissilä: Significance of background optical properties, time-resolved information and optode arrangement in diffuse optical imaging of term neonates, Physics in Medicine and Biology, 54, 3, 535-54 (2009)

J. Heiskala, T. Neuvonen, P. E. Grant, I. Nissilä:, Significance of tissue anisotropy in optical tomography of the infant brain, Applied Optics Applied Optics, 46, 10, 1633-1640 (2007)

J. Heiskala, I. Nissilä, T. Neuvonen, S. Järvenpää, and E. Somersalo:, Modeling anisotropic light propagation in a realistic model of the human head, Applied Optics, 44, 11, 2049-2057 (2005)

S. Komssi, P. Savolainen, J. Heiskala, and S. Kähkönen: Excitation threshold of the motor cortex estimated with transcranial magnetic stimulation electroencephalography, NeuroReport, 18, 1, pp. 13-16 (2007)

“Example of the use of prior anatomical information from an atlas in reconstructing brain activations from an optical imaging measurerement, simulated case”

”Probability of tissue classes from an atlas of the neonatal human head”

“Reconstruction of the simulated absorptive perturbations shown in ‘Target’ using different models.

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Simulation of Digital Breast Tomosynthesis (DBT).

Top: Orthogonal slices through an MRI volume which has been deformed using a biomechanical model to simulate DBT compression.

Bottom: Simulated DBT image created from the same volume by reconstructing simulated X-rays generated using the deformed MRI.

Detecting Change in X-ray Mammography and Digital Breast Tomosynthesis Via Model Based Registration

John Hipwell

My research is focused on improving the accuracy of both existing and novel methods to detect changes in mammograms with the aim to improve diagnosis of breast cancers.

Breast cancer is the most common form of cancer in women worldwide and will effect one in nine women at some point in their lives. Radiology is a key component in the management of the disease, from detection and diagnosis to surgery planning and monitoring the effect of treatment. Early detection of cancer has been shown to reduce mortality and is achieved using X-ray mammography screening. X-ray mammography is less effective in younger women with dense breasts however, and for these women, who may have a high genetic risk of breast cancer, MRI may be more effective. Breast ultrasound is commonly used to guide biopsies and can aid diagnosis of breast cancer by distinguishing benign tissue and cysts from cancerous lesions. MRI is established as a tool for cancer staging, surgical planning and follow-up. Tomosynthesis is an emerging X-ray modality which overcomes the projective nature of mammography by allowing the user to ‘scroll’through the breast and examine tissue at different depths.

In the breast imaging group we are in-vestigating how each of these imaging modalities can be related over time, and also, due to their complementary nature, to each other (eg. MRI to X-ray). Clearly the breast is a highly deformable structure.

Added to this is the further complication that the breast is in a very different position (prone, supine or compressed etc.) for each imaging modality.

This makes the comparison across mo-dalities a very challenging task, and a fundamental part of our work involves understanding this deformation by


Snehal M. Pinto Pereira, John H. Hipwell, Valerie A. McCormack, Christine Tanner, Sue M. Moss, Louise S. Wilkinson, Lisanne A. L. Khoo, Catriona Pagliari, Pippa L. Skippage, Carole J. Kliger, David J. Hawkes, and Isabel M. dos Santos Silva, Automated Registration of Diagnostic to Prediagnostic X-Ray Mammograms:

Evaluation and Comparison to Radiologists’ Accuracy. Medical Physics 37, 4530-4539, 2010.

L. Han et al, Fast deformation simulation of breasts using GPU-based dynamic explicit FEM, Proc. Int. Workshop on Digital Mammography, LNCS 6136, pp. 728-735, 2010.

T. Mertzanidou, J. Hipwell, M-J. Cardoso, C. Tanner, S. Ourselin and D. J. Hawkes, X-ray mammography - MRI registration using a volume-preserving

building computationally efficient (using GPU technology) bio-mechanical models of the breast.

The ultimate goal of our research is to develop tools to aid the clinician in detecting and quantifying change in the breast that may be due to malignant disease or indicative of effective treatment.

Multi-Modal Imaging Analysis forCancer Therapy Planning and Response

Albert K. Hoang Duc

Radiation therapy of cancer requires the segmentation of organs at risk on various imaging modalities in order to maximize the dose received by the target tumor while controlling the dose received by the surroundings organs at risk. Manual contouring can provide these delineations but is dramatically time consuming and prone to inter-expert variability.

My research focuses on developing novel atlas-based algorithms to obtain automatic segmentation of organs at risk in the liver and head/neck regions that produce fast, accurate and consistent results.

Atlas-based segmentation of medical images (CT, MR, PET/CT and SPECT/CT) is widely used for the diagnosis and staging of cancer and is being applied to therapy monitoring and response assessment.

Such technique is very much dependent on two parameters. First, the choice of the atlas is crucial. For instance the atlas can fail to segment some particular anatomies that are not well represented in the database. Second, the transformation used to register the atlas image to the query image is applied to the segmented atlas image to yield a segmentation of the query image. Thus the registration method greatly impacts the results.

Comparison of atlas-based segmentation (top row) with the corresponding manual labeling (bottom row). A single atlas was used. The image registration framework was composed of a linear registration (global registration) and two non-linear registration (polyaffine registration) steps. (Image from Han et al. 2008)

The objective of my research project is twofold: - analysis of large databases of patients undergoing similar imaging and treatment modalities in order to extract liver and head/neck atlases.

- development of techniques for the analysis of such large data collections, and in effect extending techniques (i.e. registration) used widely for brain region to the liver and head/neck regions.

The acquisition of large quantities of imaging studies prior to, during and after treatment may be a significant challenge for hospital logistics but offers a great opportunity for large scale analysis and data mining.

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Image Guided Interventions for Minimally Invasive Diagnosis and Treatment

Mingxing Hu

I am a senior research fellow in the Centre for Medical Image Computing at UCL. My research interests focus on computer vision technologies for image guided interventions, minimally invasive surgery and medical image analysis. I have been involved in several projects supported by Royal Society, EU FP7, EPSRC, NIHR, CRUK, DoH and industry partners. More than 30 papers have been published in international journal and conferences and three patents have been filed in the areas of imaged guided interventions.

How to use the optical cameras to provide more 3D information for preoperative and intraoperative procedures is one of the challenging research areas in image guided interventions, especially for minimally invasive surgery. My current research work focus on addressing some of these challenges with the computer vision technologies, including 3D/4D reconstruction and registration, feature analysis, information fusion, stereo reconstruction and dynamic mosaic.

We developed a new 3D reconstruction technique for the heart surface, this technique can provide a wider field-of-view with 3D information for surgeons, including recovering of the missing data. By using this new technique, the reconstructed surface can cover a large area of the heart and this helps to solve the fundamental problem of minimal invasive surgery, that is, the narrow field-of-view. We also developed a novel heart motion analysis technique using moving cameras only. With this

3D fusion of colonoscopy and CT colonography


M.X. Hu, G P. Penney, D. Rueckert, P. J. Edwards, M. Figl, F. Bello, R. Casula and D. J. Hawkes, “Reconstruction of a 3D surface from video that is robust to missing data andoutliers: application to minimally invasive surgery using stereo and mono endoscopes ”, Medical Image Analysis, (accepted).

M.X. Hu, K. McMenemy, S. Ferguson, G. Dodds and B.Z. Yuan, “Epipolar geometry estimation based on evolutionary agents”, Pattern Recognition (Elsevier), Vol. 41, No. 2, pp. 575-591, 2008.

M.X. Hu, G P. Penney, D. Rueckert, P. J. Edwards, M. Figl, F. Bello, R. Casula and D. J. Hawkes, “Non-rigid reconstruction of the beating heart surface for minimally invasive cardiac surgery”, 12th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2009), pp. 34-42.

M.X. Hu, G P. Penney, D. Rueckert, P. J. Edwards, M. Figl, P. Pratt, and D. J. Hawkes, “A novel method for heart motion analysis based on geometry estimation”, 11th International Conference on Medical Image Computing and Computer Assisted Intervention

robust technique we can convert the complicated 4D (3D+T), the almost impossible, dynamic reconstruction and registration problem back to 3D problem.

These techniques can be applied to a range of applications of minimally invasive diagnosis and surgery, such as coronary artery bypass surgery, liver surgery, colonoscopy/colonography, prostate surgery etc.

Multimodality guidance for endomicroscopy

Image guided cardiac surgery

Image guided liver surgery

Image guided laparoscopic surgery

Image guided laparoscopic surgery

MR to Ultrasound Registration for Image-guided Prostate Biopsy and Interventions

Yipeng Hu

I am currently undertaking a PhD at the Centre for Medical Image Computing at UCL, supervised by Dr Dean Barratt and Professor David Hawkes. My research interests include: motion modelling for compensating for organ motion, US-based non-rigid image registration techniques, data analysis and fusion for image-guided interventions, probabilistic approaches to image registration, modelling prostate biopsy protocols using 3D reconstructed histopathological data.

Multi-modality image registration techniques help clinicians to fuse information from different images. For instance, magnetic resonance (MR) images with rich diagnostic and/or therapeutic information may be used to target transrectal ultrasound- (TRUS-) guided prostate interventions after the MR image has been registered to the US images. Fast and deformable registration is highly desirable to accurately deliver therapy to the region of interest, since time is limited and the gland deforms during therapy (for example, due to US probe pressure).

We are interested in registering prostate MR and US images, but this is difficult because:

• non-rigid motion occur between MR and US imaging;

• the two image modalities have intrinsically different grey-level intensity characteristics, which makes difficult to apply conventional image similarity measures.

• the target US images are prone to noise and artefacts, which are highly operator dependent.

We have developed a novel model-to-image vector alignment (MIVA) approach to address some of these difficulties. This framework can be applied to several clinical applications, such as US-guided biopsies and therapies.

Before a procedure, we propose a patient-specific statistical motion model (SMM), which is trained using data provided by biomechanical simulations of gland deformation. Each simulation predicts the motion of a volumetric finite element (FE) mesh due to the random placement of a TRUS probe in the rectum. During a procedure, we employ the MIVA registration approach in which the SMM, derived from her/his MR image, is registered automatically to a TRUS volume by maximising the likelihood of a particular model shape given a voxel-intensity-based feature


Hu et al, Deformable Vessel-Based Registration Using Landmark-Guided Coherent Point Drift. MIAR 2010

Hu et al, Comparison between Transperineal and Transrectal Biopsy for the Detection of Prostate Cancer to Guide Focal Therapy. EAU 2010

Hu et al, MR to Ultrasound Image Registration for Guiding Prostate Biopsy and Interventions. MICCAI 2009


Hu et al, A Statistical Motion Model Based on Biomechanical Simulations for Data Fusion during Image-Guided Prostate Interventions. MICCAI 2008

that represents an estimate of surface normal vectors at the boundary of the gland. Using patient data and anatomical landmarks, we found that the accuracy of the registrations within 2mm is achievable. In terms of methodology, I am interested in:

• motion modelling for compensating for organ motion, such as FE based biomechanical simulation, statistical shape modelling and combined statistical-biomechanical models;

• US-based non-rigid image registration techniques, involving image processing, feature extraction and registration; and

• data analysis and fusion for image- guided interventions.

• probabilistic approaches to image registration

• modelling prostate biopsy protocols using 3D reconstructed histopathological data.

An SMM registered to the 3D intra-operative US image by matching the vector fields (model-orange, image-green).

A FE mesh of the prostate(shown in red), built from a pre-operative MR image, is used to simulate the deformation that may occur during a surgical procedure. The TRUS probe mesh is shown in blue

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Establishing Correspondence between Temporal X-ray Mammograms

My research focuses on the alignment of temporal X-ray mammographic images. In particular it addresses the deficiency of one-to-one mappings assumed by conventional 2Dmammographic alignment or registration algorithms.

Breast cancer is the most common form of cancer in women and it occurs in the glandular epithelial tissue. X-ray mammography is by far the most common modality used to screen the general population for breast cancer. The comparison of the current mammogram with the previous mammogram is used to indicate changes that may be associated with cancer as in figure 1. Of particular interest are pre-cancerous changes and increase in risk factors related to areas of dense breast tissue. The registration of temporal mammographic images is an active research topic and detailed comparison would benefit from computer assisted registration. The registration or alignment of mammographic images, however, remains a difficult and challenging problem. The complexity of this task lies with the superimposition of features from the three dimensional breast when projected on to two dimensional mammograms.

Yassir M Jafar

Fig1: X-ray mammograms: Temporal pair for a patient who self referred with a palpable mass, taken 3 years apart.

Over the years a number of mammo-graphic registration techniques have been developed however they are limited since they fail to consider the complex 3D deformations that contribute to the transformation between a pair of 2D X-ray mammograms. The transformation of the 2D projection is not one-to-one nor is it continuous, although the underlying 3D deformation is likely to be both smooth and continuous. The resulting correspondences are likely to have a many-to-many relationship among features at varying depths in the breast which are projected and superimposed in X-ray mammograms.

My research focuses on establishing realistic 3D transformations which cannot be accounted for using 2D one-to-one correspondences. This work supports the development of more accurate registration algorithms by taking into account the realistic 3D movement of breast tissue.

Use of Anatomical Data forEmission Tomography Reconstruction

Dr. Daniil Kazantsev

My research focuses on iterative reconstruction techniques for emission tomography, SPECT in particular. The use of anatomical information as a prior for image reconstruction is the main topic of interest.

Due to the lack of the higher sensitivity and better spatial resolution of the SPECT system, obtained projection data is commonly poorer and quality of estimated activity is worse than for PET. Possession of additional information about solution in this case can be noticeably valuable. Data available from other modalities, like CT/MR, can be used as prior information about the anatomical structure of the studied object. Hybrid machines like SPECT/CT, PET/MRI are engaged worldwide in practice and SPECT/MRI gantry is currently under development.

For reconstruction purposes we use a Bayesian framework or maximum a posteriori (MAP) method for the SPECT. With MAP approach it is possible to incorporate information from other scanning modalities (MR, CT) as a prior knowledge about seeking activity distribution.

The main aim here is to use a specific regularization function for prior which is satisfied emission image properties (expected to be locally smoothed while allow discontinuities).

My work is focused on composing a suitable prior term for MAP-EM iterative scheme. Different priors have been investigated during our research. As recent publications have suggested, information theory similarity metrics, such as joint entropy (JE) and mutual information (MI), can successfully embed as anatomical prior into Bayesian reconstruction. New modifications with a JE prior gave some enhancement in reconstructions.

Anatomically driven Bowsher prior (BP) has a potential to considerably enhance the quality of the emission


D. Kazantsev, S. Pedemonte, A. Bousse, C. Panagiotou, S.R. Arridge, B.F. Hutton and S. Ourselin., “ET Bayesian Reconstruction using Automatic Bandwidth Selection for Joint Entropy Optimization”. Accepted for NSS-MIC 2010.

D. Kazantsev, S. Pedemonte, A. Bousse, S.R. Arridge, B.F. Hutton and S. Ourselin., “Automatically Thresholding Bowsher Prior with Nonlocal Weighting for 3D SPECT Reconstruction” Work with paper in progress. A reconstructed detailed region of 3D

brain phantom, from left to right: true activity, ML-EM reconstruction, BP, ATBP.

image without computational expensive segmentation tasks and probability density estimations. Convex functions to apply smoothness for BP, are lacking for edge-preserving characteristics and it is a vulnerable aspect of the technique. In a straightforward modification of the BP (ATBP), proposed by us, discontinuities can be maintained while still allowing locally smoothing effect on the same tissue class. We used a nonlocal algorithm for mutual weights estimation required for the new prior, it helps to retain uncorrelated information in emission distribution while following the anatomical map. It is an important issue to consider, since absolute reliance on anatomical data can lead to biased solutions neglecting significant features in activity estimate, such as lesions.

PET/CT scanning gantry

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Diffusion tensor MRI in longitudinalAlzheimer’s disease

My research focuses on methodological analysis and developing methods using DTI on longitudinal study of Alzheimer’s disease

Diffusion tensor imaging (DTI) is a MRI technique that allows for the interrogation of the microstructural integrity of white matter (Fig.1). Based on increases in translational diffusion (mean diffusivity: MD) and decreases directional diffusion (fractional anisotropy: FA) damage to white matter can be assessed (Fig.2).

There is evidence from animal, pathological and imaging studies that disruption of white matter (WM) occurs in the course of Alzheimer’s disease (AD) and may be an early event such as cases destine to develop the disease by genetic alterations (familial Alzheimer’s disease or FAD). DTI is an evolving technique and ad-vances in its application ought to provide new insights into neuronal diseases such as AD. Despite the mounting evidence for WM impairments in AD, no study has examined progressive WM abnormalities with DTI using longitudinal, within-subjects design.

I joined Dementia Research Centre (DRC), which is a multidisciplinary group conducting research into the diagnosis and treatment of dementia, in June 2010. My post is

Shiva Keihaninejad

Fig.1. Visualisation of a diffusion tensor imaging measurement of a human brain. Depicted are reconstructed axon tracts that run through the mid-sagittal plane. Especially prominent are the U-shaped fibres that connect the two hemispheres through the corpus callosum.

part of an ongoing study to use DTI to characterise neurodegeneration in FAD and related disorders. This study involves analysis of a unique longitudinal data set to provide critical insights into the early stages in AD.

My research is focused on: 1) Analysing the currently available methods such as Tract-based spatial statistics (TBSS) and DTI tractography and providing them as applicable tools in clinical studies. TBSS is an automated observer independent approach for assessing group-wise micro-structural differences in the major white matter pathways of the brain (Fig.3). DTI tractography provides a potentially valuable tool to assess connectivity in vivo.

2) Developing new methods to study the longitudinal changes of WM in AD.

Fig.2. Fractional anisotropy image of a patient with AD.

Fig.3. Group difference map between control subjects and patients with posterior cortical atrophy (PCA), the visual variant of Alzheimer’s disease, for skeletonised diffusion FA using TBSS software. The crosshair is positioned on a significant cluster near the fornix. Yellow-red areas show significant regions for group difference.

Improving Biomarkers for Neuro-Degenerative Diseases

Kelvin K. Leung

My research interests are in the development of automated, sensitive and robust image biomarkers for neuro-degenerative diseases, with the aims to improve drug trials and early diagnosis of the diseases.

Large multi-site clinical studies provide a powerful way to understand diseases and their treatments. The large numbers of images from different sites and scanners brings particular challenges for image analysis algorithms. It is highly desirable to have automated, sensitive and robust imaging biomarkers that can correctly track the disease progression in large numbers of multi-site images.

The boundary shift integral (BSI) is a semi-automated measure of regional and global cerebral atrophy rates from serial MRI. In order to accurately measure brain atrophy using BSI, the intensity of the same tissue in the baseline and repeat scans should be as similar as possible. However, there could be significant changes in intensity over time in multi-site studies that last several years. KN-BSI was proposed to address differences in tissue contrast over time and between scanners using robust intensity normalisation and automatic parameter selection based on the intrinsic tissue contrast of the MR images. Volume and change in volume of the hippocampus are both important markers of Alzheimer’s disease (AD).

Delineation of the structure on MRI is time-consuming and therefore reliable automated methods are required. A technique, called multiple-atlas


Leung K. K., Clarkson M. J., Bartlett J. W., Clegg S., Jack C. R., Weiner M. W., Fox N. C., Ourselin S., ADNI (2010). Robust atrophy rate measurement in Alzheimer’s disease using multi-site serial MRI: Tissue-specific intensity normalization and parameter selection. Neuroimage 50(2), 516-523

Leung, K. K., Barnes J., Ridgway G. R., Bartlett J. W., Clarkson M. J., Macdonald K., Schuff N., Fox N. C., Ourselin S. (2010). Automated cross-sectional and longitudinal hippocampal volume measurement in mild cognitive impairment and Alzheimer’s disease. NeuroImage 51(4), 1345-1359

Figure 2: An example of the hippocampal segmentation from MAPS.

propagation and segmentation (MAPS), is proposed to automatically and accurately delineate the hippocampus.

The improved technique uses non-linear registration of the best-matched templates from a manually segmented library to generate multiple segmentations and combines them. Hippocampal atrophy is measured by applying BSI over MAPS regions. The source code of the software can be found at http://sourceforge.net/projects/bsintegral.

Figure 1: Results of intensity normalisation using classic-BSI (left) and KN-BSI (right). The figure shows the subtraction images of the baseline and repeat scans of the same subject. Notice the subtle change in contrast between CSF and GM/WM in the ventricles.

Figure 3: Comparison of hippocampal volume and atrophy rate in controls (n=200), mild cognitive impairment (MCI) (n=335) and AD (n=147) subjects. The figures show expected patterns of volume (AD < MCI < control), and atrophy rate (AD > MCI > control).

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Real-Time Tracking of Lung Tumours for Radiotherapy Applications

My EngD research looks to incorporate the effects of respiratory motion into Radiotherapy treatment of lung tumours. Real-time knowledge of tumour position is becoming ever more important as treatments grow more targeted and ambitious.

Radiotherapy uses x-rays to permanently damage the DNA of cancerous cells within tumours, preventing their growth. It is often used in conjunction with other treatments, such as surgery or chemotherapy. Compared to conventional Radiotherapy, Stereotactic Body Radiotherapy (SBRT) is able to treat patients in fewer visits by delivering a larger dose in a more targeted way. New technology currently being developed, such as tracked- or gated-based methods, will rely on real-time tracking of tumour position.

With SBRT, Guy’s and St. Thomas’ Trust currently use a 4DCT scan to plan and an Elekta Synergy machine to confirm tumour alignment and treat. During treatment the patient is left to breathe freely and patient motion is not monitored. Having real-time knowledge of tumour position during treatment would allow more of the beam to be delivered to the tumour as well as reducing differences between planned and actual treatments.

James Martin

Picture of the Elekta Synergy machine at Guy’s and St. Thomas’ Trust and Vision RT surface data of the thorax region

In conjunction with Vision RT, a world leader in 3D surface imaging equipment, information acquired during treatment will be used to determine tumour position and size. Latest research (Cancer UK) indicates that only 8% of patients survive longer than 5 years after diagnosis of lung cancer, making the motivation of this research a strong one.

Financial support provided by the EPSRC and Vision RT.


McClelland et al, A continuous 4D motion model from multiple respiratory cycles for use in lung radiotherapy. Medical Physics 2006

Jaffray et al, FLAT-PANEL CONE-BEAM COMPUTED TOMOGRAPHY FOR IMAGE-GUIDED RADIATION THERAPY. Int. J Radiation Oncology Biol. Phys. 2002


McClelland et al, Registration Based Respiratory Motion Models for use in Lung Radiotherapy. PhD Thesis, 2008

Respiratory Motion Modelling for Use in Radiotherapy

Jamie McClelland

I am a research associate at CMIC, UCL. I completed my PhD in the modelling of respiratory motion for lung radiotherapy in May 2008, also undertaken at CMIC. Since then I have been continuing my research in this area, and also working on CT registration for colonoscopy. I am currently a subsidiary supervisor for an EngD student working on the respiratory motion (James Martin), and a PhD student working on the colon CT registration (Holger Roth).

Respiratory motion is a major factor contributing to errors and uncertainties in Radiotherapy (RT) treatment of lung tumours. Accurate models of the respiratory motion would be very useful for planning and guiding RT, and may enable more effective treat-ment to be delivered.

We have developed and evaluated methods of building patient specific respiratory motion models from Cine CT data. These relate the internal motion to an external surrogate signal (such as the displacement of the skin surface, Figure 1) that can be easily measured during data acquisition and treatment delivery.

The models offer a number of advantages over current methods of imaging and analysing respiratory motion, in particular their ability to account for some breath-to-breath variations in the respiratory motion. This means that the models can provide a better estimate of the true appearance of the internal anatomy than 4DCT, which is the current clinical standard used to plan lung RT - Figure 2.

However, there can be much larger day-to-day variations in the respiratory motion between different fractions of RT treatment – Figure 3. The models (and clinical 4DCT) are currently unable to predict these variations. Therefore we are now investigating methods of updating or re-building the motion models so that they can account for both the intra- and interfraction variations. We are also trying to develop methods of constructing the motion models from x-ray projection data, as this can be acquired very quickly so is free of the motion artefacts found in reconstructed 3D CT (and MR) volumes.

This work is being undertaken in close collaboration with clinicians and medical physicists at Guy’s and St. Thomas’ Hospitals, and the Institute

Figure 2 – top: clinical 4DCT, bottom: volume predicted by motion model


J McClelland, S Hughes, M Modat, S Ahmad, D Landau, S Ourselin, D Hawkes, “Study of inter-fraction variations in respiratory motion using deformable registration based motion models,” Proc. of ICCR 2010.

J McClelland, S Webb, D Binnie, and D Hawkes, “Tracking ‘differential organ motion’ with a ‘breathing’ multi-leaf collimator: magnitude of problem assessed using 4D CT data and a motion compensation strategy,” Phys. Med. Biol. 52, 2007.

J McClelland, A Chandler, J Blackall, S Tarte, S Hughes, S Ahmad, D Landau, D Hawkes, “A Continuous 4D Motion Model from Multiple Respiratory Cycles for Use in Lung Radiotherapy,” Med. Phys. 33, 2006.

of Cancer Research and the Royal Marsden Hospital.

Figure 1 – 3D skin surface used to generate a respiratory

Figure 3 – top: example of interfraction variation.

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Contrast Enhanced MRI Motion Correction

Andrew Melbourne

The retrospective alignment of images in a dynamic contrast enhanced series is made difficult by time-varying tissue contrast. Fortunately, methods can be devised that separate the effects of subject motion from contrast enhancement. Removal of motion artefacts enables improved contrast-uptake model-fitting and pharmacokinetic parameter estimation for improved diagnosis and therapy.

DCE-MRI: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a widely used technique for quantitative assessment of the vascular properties of tissue. This has particular application in oncology and the assessment of tumour tissue. In many organs, a growing tumour will soon require a substantial blood supply and to do this it stimulates arterial growth. The new arterial growth will be disordered and under-developed, promoting vascular leakage. If a small molecular weight intravenous vascular contrast agent is administered, inspection of the local rate-of-change of tissue contrast associated with this leakage allows the differentiation of regions with otherwise homogeneous contrast on a standard T1-weighted MRI. The pattern of enhancement yields quantitative information that allows the assessment of pharmacokinetics in both single studies and in sequential studies (i.e screening).

Image Registration: Patient movement between contrast enhanced images may be corrected using an automated image alignment (registration)algorithm. However, some adaptations are required so that the registration algorithm is not biased. General registration methods consist of two operational parts: a measure of image similarity is maximised and the applied transformation is assessed relative to an ideal set of transformations[3].


Melbourne, A.; Atkinson, D.; White, M. J.; Collins, D.; Leach, M. & Hawkes, D. Registration of dynamic contrast-enhanced MRI using a progressive principal component registration (PPCR). Phys Med Biol, 2007, 52, 5147-5156.

Melbourne, A.; Hipwell, J. & Hawkes, D. The effect of motion correction on pharmacokinetic parameter estimation. International Workshop on Digital Mammography, Springer-Verlag, 2010, 744-751.

Melbourne, A.; Ridgway, G. & Hawkes, D. J. Image Similarity Metrics in Image Registration. Proceedings of SPIE Medical Imaging, 2010.

Melbourne, A.; Hawkes, D. & Atkinson, D. Data Driven Groupwise Registration of Diffusion Weighted Images. Proceedings of ISBI, 2010.

In the case of DCE-MRI, the transformation is likely to be one that does not allow volume change since the time between images is short, thus this may be implemented as a constraint. The image similarity can be optimised given a model of the contrast enhancement mechanism, increasing sensitivity to motion artefacts in preference to contrast changes.

I am currently a post-doctoral research associate working on improved imaging for diagnosis of breast cancer funded by EPSRC grant EP/E031579/1 and working closely with the HAMAM FP7 collaboration.

Highly Accurate Breast Cancer Diagnosis through Integration of Biological Knowledge, Novel Imaging Modalities and Modelling

Thomy Mertzanidou

I am a PhD student, working under the supervision of Professor David Hawkes and Dr. John Hipwell. Previously I obtained an Electrical and Computer Engineering Diploma from the Aristotle University of Thessaloniki and in 2008, I completed an MSc course in Computer Graphics, Vision and Imaging (UCL).

This EU project tackles the problem of early detection and accurate diagnosis of breast cancer, by integrating different imaging modalities and patient information, into a single workstation. Our area of research is the development of algorithms for automatic spatial correlation between modalities, and in particular, for this project, between X-ray mammography and Magnetic Resonance Images (MRI).

X-ray mammography is the standard modality for breast cancer screening and is also used in symptomatic clinics to diagnose suspicious lesions. It produces high resolution images which reveal fine structure in the fibroglandular tissue, and microcalcifications that can be indicative of malignant disease. In women with dense breasts, such as young women who may have a high genetic risk of breast cancer, features in the breast can be obscured by overlying tissue imaged using X-rays. In these cases MRI has been found to be a superior diagnostic modality and is

Registration results (2 cases/rows). From left to right: projection of the source volume before registration, after registration and real X-ray mammogram. The projection images are simulated mammograms, using the MR volume. The red cross indicates the position of a corresponding coordinate in each image.


Thomy Mertzanidou, John Hipwell, Manuel Jorge Cardoso, Christine Tanner, Sebastien Ourselin, David Hawkes, “X-ray Mammography - MRI Registration Using a Volume-Preserving Affine Transformation and an EM-MRF for Breast Tissue Classification”, Digital Mammography / IWDM, p. 23-30, 2010.

Thomy Mertzanidou, John Hipwell, Christine Tanner, David Hawkes, “An intensity-based approach to X-ray mammography - MRI registration”, Proc. SPIE Medical Imaging: Image Processing, 7623-106, 2010.

commonly used to clarify findings which are unclear using X-ray mammography.

MRI extracts functional information from the breast and this enables its use for preoperative “staging”, to determine lesion size, lymph node involvement and the metastatic status of breast cancer. X-ray and MRI can therefore be viewed as complementary modalities, which, if aligned, could aid radiologists in the diagnosis, staging and surgical planning for treatment of breast cancer.

We are tackling this registration task of aligning an X-ray mammogram with an MRI, from the same patient, using an intensity-based approach. The MR volume is used to simulate an X-ray image that can then be directly compared with a real mammogram. We are also working on the integration of several transformation models to our registration framework, including affine and quadratic transformations, biomechanical modelling and statistical deformation models.

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Near Real-Time Non-RigidImage Registration

Marc Modat

My research is mostly driven by clinical questions raised by dementia such as Alzheimer’s disease with a focus on biomarkers. I have also had the opportunity to work on other applications such as lung, colon, intervential MRI and small animal imaging.

Non-linear registration is a tool commonly used in medical image analysis. However techniques are usually time consuming. Graphics Processing Units (GPUs) achieve a high floating point capacity by distributing computation across a high number of parallel execution threads. This computational capacity can be used to dramatically decrease the computation time of well-known algorithms provided they can be mapped to a parallel architecture. I have developed a parallel version of the well-known free-form deformation algorithm and implemented it using the CUDA API from NVidia. Execution time falls from a few hours to less than a minute with similar accuracy.

Further methodological work involved biomechanically constrained deformation using finite element model, differential bias field correction or topology preservation for registration purpose and diffeomorphic registration.

This average has been built using a groupwise technique which involved 19 specimens. The 19 specimens have been prepared and the data acquired at the Centre for Advanced Biomedical Imaging (CABI) using a 9T MR scanner.


M. Modat, G. R. Ridgway, Z. A. Taylor, M. Lehmann, J. Barnes, D. J. Hawkes, N. C. Fox, and S. Ourselin, “Fast free-form deformation using graphics processing units,” Comput Meth Prog Bio, vol. 98, no. 3, pp. 278–84, 2010.

M. Modat, J. McClelland, and S. Ourselin, “Lung registration using the NiftyReg package,” in MICCAI workshop: Evaluation of Methods for Pulmonary Image Registration, 2010

A follow-up scan (top image) from an Alzheimer’s disease patient has been registered to its baseline scan. The second image shows the intensity differences, which have to be recovered.

The recovered warping can be used to generate the following Jacobian determinant map. It corresponds to the rate of volume change occurring over time.

Open source software

Nifty Reg contains programs to perform rigid, affine and non-linear registration of medical images. Two versions of the algorithms are included, a CPU- and a GPU-based implementation. The code can be downloaded from http://sourceforge.net/projects/niftyreg

A Spatial Variation Model for White Matter Microstructure

Gemma Morgan

I am a PhD student at the Department of Computer Science at University College London. My work is supervised by Dr Daniel Alexander and my main area of research is diffusion MRI. Specifically, I am investigating how diffusion MRI can be used to detect and map white matter abnormalities in schizophrenia. I am also involved in Camino, a free, open-source, object-oriented software package for analysis and reconstruction of diffusion MRI data, tractography and connectivity mapping. Please feel free to contact me to find out more about my work.

Diffusion Magnetic Resonance Imaging (MRI) is a powerful, non-invasive technique which measures the displacement of water molecules in vivo. Over the past 15 years, it has become an essential tool to measure changes in brain microstructure due to pathology. Indices derived from diffusion MRI such as Fractional Anisotropy (a measure of the directionality of diffusion) and Mean Diffusivity (the average diffusivity) are routinely used in clinical studies; however, they lack specificity and cannot be directly related to changes in underlying tissue microstructure. To do this we need more biologically relevant measures. Possible candidates for these new markers include axon radius and axon density.

Registration results (2 cases/rows). From left to right: projection of the source volume before registration, after registration and real X-ray mammogram. The projection images are simulated mammograms, using the MR volume. The red cross indicates the position of a corresponding co-ordinate in each image.


G Morgan, R D Newbould, B Whitcher and D C Alexander, Polynomial models of the spatial variation of axon radius in white matter, Proceedings of the International Society of Magnetic Resonance in Medicine (2010)

G Morgan, H Zhang, B Whitcher and D C Alexander, A spatial variation model of white matter microstructure, Computational Diffusion MRI workshop, MICCAI (2010)

Recent work by Alexander et al measures both the axon radius and density in the corpus callosum of live human subjects. However the hardware requirements for the technique are not available clinically and the resulting estimates were noisy.

My work aims to improve estimates of these parameters by exploiting the spatial coherence of axon radius and density across the corpus callosum. By incorporating spatial information in our model, we will be able to make more accurate and precise estimates of these important new microstructural parameters from measurements obtained on clinical scanners.

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Motion correction inDynamic MRI

Freddy Odille

I received my PhD degree from Nancy University, France, in 2007. I have recently spent the last three years as a research associate with CMIC. My research interests include signal processing and mathematics applied to adaptive MRI acquisition, reconstruction and post-processing methods. I am a member of the International Society for Magnetic Resonance in Medicine.

My research within CMIC initially developed his GRICS motion correction algorithm to use the extra data available from multiple receiver coils and k-space lines added to a cardiac CINE acquisition. Signal processing of this data provided information on timings within the cardiac and respiratory cycles. These were used within the GRICS method to produce artefact free images from a free breathing volunteer.

Through an understanding of the motion and the reconstruction, I was able to split the reconstruction into separate components that could be reconstructed in parallel using a computer cluster, demonstrating significant speed ups.

The GRICs method contains an optic flow based registration step and this has been exploited recently for segmentation propagation to analyse real-time blood flow data . This has been implemented as a plug-in for the OsiriX viewing tool enabling easy use by clinicians for cardiac applications including blood flow analysis and segmentation of the ventricles.

Cardiac images from a free breathing volunteer without ECG monitoring. Uncorrected (top) and corrected (bottom) using motion information from extra k-space profiles and multiple receiver coils as input to a coupled optimisation technique.


Odille, F., Batchelor, P. G., Prieto, C., Schaeffter, T., Atkinson, D. (2010). A parallel computing framework for motion-compensated reconstruction based on the motion point-spread function. ISMRM. ( pp.498).

Odille, F., Batchelor, P. G., Prieto, C., Schaeffter, T., Atkinson, D. (2010). On the accuracy of nonrigid motion correction. ISMRM Workshop on Motion Correction, Kitzbuhel, Austria.

Odille, F., Steeden, J. A., Muthurangu, V., Atkinson, D. Automatic segmentation propagation of the aorta in real-time phase contrast MRI using nonrigid Registration. JOURNAL OF MAGNETIC RESONANCE IMAGING [Accepted]

Odille, F., Uribe, S., Batchelor, P. G., Prieto, C., Schaeffter, T., Atkinson, D. (2010). Model-based reconstruction for cardiac cine MRI without ECG or breath holding. Magnetic Resonance in Medicine 63(5), 1247-1257.

Odille, F., Uribe, S., Schaeffter, T., Atkinson, D. (2009). Cardiac and respiratory motion compensated reconstruction driven only by 1D navigators. ISMRM 2009. pp.4644.

Odille, F., Vuissoz, P., Felblinger, J., Atkinson, D. (2008). GENERALIZED RECONSTRUCTION BY INVERSION OF COUPLED SYSTEMS (GRICS) APPLIED TO PARALLEL MRI. 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), Paris, France. ( pp.1019-1022).

Geometric Brain Tissue Models: From Cylinders to High Fidelity 3D Meshes

Eleftheria Panagiotaki

I am a research student working as part of the Micro- structure Imaging Group which is a member of the department of Computer Science and in the Centre for Medical Image Computing (CMIC) at UCL.

Because biological tissue structures directly influence the dispersion patterns of water molecules within them, thus measuring this dispersion allows us to make inferences about the tissue microstructure. This work simulates the dispersion of water molecules within biological tissue models. We compare the predictions of our models with an alternative method of measuring dispersion, called diffusion-weighted MRI (DW-MRI).

We model the DW-MRI signal within brain white matter tissue. We are interested in deriving microstructure indices such as white matter axon di-ameter and density from our models. We use analytical and numerical models to investigate which aspects of the tissue and properties of the diffusion process affect the diffusion signal.

Analytic models are computationally efficient and describe the diffusion

A) DW-MRI of human brain indicating the corpus callosum, B) white matter microstructure, C) analytical models of the diffusion signal in brain white matter, D) DW-MRI of a biological phantom (asparagus), E) CLSM image of the same sample, F) three-dimensional mesh model shown from two different angles.


[1] Panagiotaki E, Fonteijn H, Siow B, Hall M.G, Price A, Lythgoe M.F, Alexander D.C. Two-compartment models of the diffusion MR signal in brain white matter. MICCAI London: Springer (2009)

[2] Panagiotaki E, Hall M.G, Zhang H, Siow B, Lythgoe M.F, Alexander D.C. (2010). High-fidelity meshes from tissue samples for diffusion MRI simulations. MICCAI China: Springer (2010)

process by approximating the tissue with simple geometry such as cylinders or spheres [1].

Because of the simplicity of these models they can be used on whole-brain data sets and provide biologically meaningful microstructure indices.

This work also studies the construction of highly accurate mesh-based virtual models of tissue from Confocal Laser Scanning Microscopy (CLSM) images [2]. Random walk simulations within the resulting three-dimensional meshes provide realistic synthetic diffusion MRI measurements.

Sophisticated data synthesis is essential for testing and developing diffusion MRI algorithms as well as investigating subtle effects such as permeability that analytic models cannot capture.

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QuantitativeEmission Tomography

Stefano Pedemonte

In my PhD at the Centre for Medical Image Computing I am investigating algorithms for quantitative activity estimation in Emission Tomography and multimodality enhanced reconstruction.

Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) are functional and molecular imaging techniques that find application in diagnosis of ischemic heart disease, mapping of local brain metabolism, drug development and detection of tumours and areas of infection.

3-D images of spatial distribution of a pharmaceutical in PET and SPECT are reconstructed indirectly, by observing, by means of Gamma Cameras, the radiation emitted by a radioisotope that is bound to the drug.

State of the art algorithms apply probabilistic inference to estimate the pharmaceutical distribution that most likely generated the radiation measured by the PET/SPECT machine. Quantitative estimation of the pharmaceutical density is limited by the use of approximate models of radiation propagation and of the imaging system. Comparison of algorithms for MRI-

enhanced SPECT reconstruction.

The complexity involved in the propagation of radiation through matter imposes adoption of an approximate model, imposing a trade-off between computational complexity of the reconstruction algorithm and image accuracy.

I am working on the design of a reconstruction algorithm based on a scalable system model that adapts to the available computational power, maximizing image quality for the given resources. The implementation is focused particularly on the SIMD architecture as it has proven efficient for tomographic reconstruction.

I am also investigating the correlation of drug distribution with the underlying tissue morphology and the use of information from other imaging modalities, such as MRI and CT, to improve the quantification of drug distribution.

Bayesian Networks provide a powerful formalism to formulate models for multimodal image reconstruction.

Software

NiftyRec is a software for stochastic iterative emission tomographic reconstruction. It is entirely GPU accelerated, achieving PET/SPECT reconstruction within seconds. It provides a C API and a Matlab Toolbox for MLEM, OSEM and MAPEM reconstruction. It is free and open source.

http://niftyrec.sourceforge.net


Pedemonte et al., GPU Accelerated Rotation Based Emission Tomography Reconstruction. IEEE NSS/MIC 2010.

Pedemonte et al., Class Conditional Entropic Prior for MRI Enhanced SPECT Reconstruction. IEEE NSS/MIC 2010.

Light Transport on an Infant Head

Surya Mohan Prerapa

I am a Research Associate in the Department of Computer Science at the University College London working on Optical Tomography since June 2009. My research interests lie in three main areas:

(1) computational modelling of deterministic and stochastic systems governed by partial differential equations (2) optical tomography, and (3) high performance computing. I would like to establish a firm foothold in biophysical and biomedical modelling.

I am interested in the mathematical models and numerical simulations of light propagation through imaging specimens such as brain, breast, etc. These are the so-called forward models which are in turn employed in image reconstruction. Light propagation models do not admit analytical solutions, except for a few simple domains. Hence numerical schemes are usually employed in reality. Accurate 3D models both in terms of capturing the physics of light transport in the media and numerical schemes are of critical importance, in order to obtain good quality reconstructions.

Finite Elements have been the method of choice due to their employability for complex domains. Here an imaging specimen is obtained as a segmented image with source and detector locations and a finite element mesh is constructed. Then a system of equations relevant to our prior knowledge of the specimen (for


P. Surya Mohan, V. Y. Soloviev and S. R. Arridge, ‘’Discontinuous Galerkin method for forward modeling in optical diffusion tomography”, International Journal for Numerical Methods in Engineering , 2010

P. Surya Mohan, P. B. Nair and A. J. Keane, ‘’Stochastic projection schemes for deterministic linear elliptic partial differential equations on random domains,’’ International Journal for Numerical Methods in Engineering, 2010

R. Bryan, P. Surya Mohan, A. Hopkins, F. Galloway, M. Taylor, P. B. Nair, “Statistical modelling of the whole human femur incorporating geometric and material properties,” Medical Engineering and Physics, 2009

Motion model from registration of training data (top). Free-breathing motion-blurred liver image (middle) and corrected using motion model (bottom).White et al. Magn. Reson. Med. 62:440-449 (2009).

example diffusion approximation for highly scattering regime, radiative transport equation for low scattering regime and hybrid models for variable scattering regimes) are solved.

The accuracy of the forward solution, however, is tied up to the mesh resolution, in addition to the large computational requirements of 3D models. Increasing the mesh resolution improves the accuracy but with an additional computational burden. In addition heterogeneities and refractive index mismatches present their own set of challenges. We have some exciting ideas to improve and accelerate the current established solvers by many-fold so as to make them available for real-time imaging.

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Motion Compensation Techniques for High-Intensity Focused Ultrasound

Erik-Jan Rijkhorst

I am a Research Associate at the Centre for Medical Image Computing. My Ph.D. was on massively parallel simulations of supersonic astrophysical gas dynamics. For my current research I develop motion compensation techniques for high-intensity focused ultrasound treatment of liver cancer.

Current approaches for non-invasive liver interventions, such as high-intensity focused ultrasound, are often limited by internal organ motion. We are investigating the use of fast ultrasound and MR image guidance techniques to quantify and compensate for respiratory motion of the liver during an intervention.

Minimally- and non-invasive liver interventions for cancer treatment, such as radio-frequency ablation and high-intensity focused ultrasound (HIFU), rely more and more on accurate image guidance. Such techniques can be used compensate for intra-operative respiratory motion, thereby allowing for a more precise treatment of a target region.

Over the past few years, several methods for quantifying organ motion using MR-based motion models have been presented. Such models can be used in combination with intra-operative ultrasound imaging to spatially align pre-operative images to the patient’s anatomy during an intervention.

We have developed several techniques for registering and synchronising dynamic ultrasound to patient-specific MR-derived motion models for the purposes of assessing spatial misalignment and temporal synchronisation.

We collaborate with research groups at King’s College London, the Institute for Cancer Research, the University of Oxford, the National Physical Laboratory, and Imperial College London.

Example of a high-resolution liver MR scan obtained during breath-hold.


Erik-Jan Rijkhorst, Daniel Heanes, Freddy Odille, David Hawkes, Dean Barratt. Simulating Dynamic Ultrasound Using MR-derived Motion Models to Assess Respiratory Synchronisation for Image-Guided Liver Interventions.

IPCAI proceedings, LNCS 2010, Vol. 6135, pp. 113-123

Yipeng Hu, Erik-Jan Rijkhorst, Richard Manber, David Hawkes, Dean Barratt. Deformable Vessel-based Registration using Landmark-guided Coherent Point Drift. MIAR proceedings, LNCS, 2010, Vol. 6326, pp. 60-69

A simulated dynamic ultrasound image of the liver used to synchronise and align a motion model during intervention. The displacement of the diaphragm is measured within the rectangular region and used to derive a temporal breathing signal.

Lung, liver, vascular, and kidney anatomy extracted from an MRI scan. The white wedge represents the simulated ultrasound image.

Cylindrical registration of the inner colon surfaces from CT colonography

Holger Roth

I am currently undertaking a PhD in the Centre for Medical Image Computting at UCL funded by Medicendico Ltd. My research focuses on establishing the spatial correspondence between inner colon surfaces extracted from CT colonography. This will help the radiologist to detect colorectal cancer more quickly and accurately.

Each year, more than 630,000 people worldwide die from colorectal cancer. The cancer in the colon can develop from colorectal lesions, mostly small polyps. Computed tomography colonography (CTC), or virtual colonoscopy is now established as standard screening tool for these colorectal lesions in USA, Europe and Japan. If a lesion is detected early enough, it can be safely treated by removing the polyp using an endoscope.

The routine is to acquire two CT images in prone and supine positions of the patient in order to increase the radiologist’s confidence in classifying a tissue as a polyp. However, this changing of the positions of the patient introduces large deformations of the colon. Its changes in inflation and orientation make it difficult and tedious for even the most experienced radiologist to establish correspondence manually. This can lead to significant delays and errors in the diagnosis.

An automatic method for establishing spatial correspondence between the prone and supine CTC views has the potential to improve accuracy and confidence of the radiologist during the screening process and save time in the diagnosis. Furthermore, its result could be included to computer-aided detection (CAD) algorithms in order to improve their robustness and accuracy.

We develop a cylindrical registration approach of the inner colon surfaces extracted from CTC. We map the prone and supine surfaces into a cylindrical domain and establish spatial correspondence non-rigidly using a cylindrical b-spline registration.


Roth H, McClelland J, Boone D, Hu M, Ourselin S, Slabaugh G, Halligan S, Hawkes D. (2010) Conformal Mapping of the Inner Colon Surface to a Cylinder for the Application of Prone to Supine Registration, 14th conference on Medical Image Understanding and Analysis (MIUA )

Roth H, McClelland J, Modat M, Boone D, Hu M, Ourselin S, Slabaugh G, Halligan S, Hawkes D. (2010) Establishing Spatial Correspondence between the Inner Colon Surfaces from Prone and SupineCT Colonography, 13th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI

PatentApparatus and Method for Registering Medical Images Containing a tubular organ, filed in March 2010

Polyp in a prone (top) and supine (bottom) CT scan.

The principle of colon surface registration using a cylindrical representation, where the color scale indicates the shape index on the colon surfaces.

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Microstructure Driven Global Tractography and Synthetic Tissue Analogues

Matt Rowe

I am a research student working as part of the Microstructure Imaging Group and in the Centre for Medical Image Computing (CMIC) at UCL.

My research interests are in the area of microstructure estimations using diffusion imaging.

Tractography has provided unique insight into structural connectivity in the brain using non-invasive diffusion weighted magnetic resonance imaging (DW-MRI). Previous tractography methods follow tensor eigenvectors through DW-MR images to find the path of least resistance to diffusion and infer the location of physical connections. Approaching connectivity mapping this way is, however, flawed by phenomena such as crossing fibers which can misdirect or terminate traced pathways erroneously. To address this limitation recent approaches have tackled connectivity mapping on a macroscopic scale, over the whole brain. These approaches invoke trial fiber configurations over the whole brain and modify the whole configuration simultaneously to best fit the diffusion data. This has the advantage of pooling data from multiple image voxels when testing the fitness of estimated connections.

Figure: Synthetic phantom based on the optic chiasm, showing restructuring of microstructure distribution


A Sherbondy, M Rowe, D Alexander Microtrack: An Algorithm for Concurrent Projectome and Microstructure Estimation MICCAI China. Springer 2010

The most basic model for diffusion in brain tissue is the diffusion tensor. This provides useful markers of tissue structure such as mean diffusivity and fractional anisotropy. However, more sophisticated models have been devel-oped which relate the MR signal to geometrical properties of the tissue such as axon diameter, packing density, intra and extra-cellular volume fraction, which can be informative measures of connectivity or markers of white matter integrity or pathology. By combining tractography on a global scale with more sophisticated DW-MRI signal models, we can improve on the results of both previous global tractography approaches and voxel-wise estimates of microstructure parameters. For this work, synthetic tissue analogues (phantoms) have been developed with microstructural tissue parameters to test the benefits of microstructure driven global tractography and provide a basis for tuning and validating algorithms in development.

Spinal Cord Diffusion Imaging: Challenging Characterisation and Prognostic Value

Torben Schneider

My project is joint between the NMR Unit at the UCL Institute of Neurology and the Microstructure Imaging Group at the Centre of Medical Image Computing and aims to improve characterisation of spinal cord white matter pathways and introduce imaging biomarkers sensitive to axonal damage and functional recovery through modelling of axonal regeneration and collateral sprouting.

Magnetic Resonance Imaging has shown to be very useful in evaluating several aspects of spinal cord injury but traditional MRI has limited predictive value because it cannot readily distinguish important fine structural detail within the cord. Diffusion MRI is a very promising technique that is sensitive to microstructural tissue characteristics but it has yet to be applied to the spinal cord. We investigate the potential of existing methods like diffusion tensor imaging and develop new techniques that are more sensitive to specific tissue characteristics like axon diameter and density to help understanding the underlying structural changes in spinal cord injury.

High resolution diffusion MRI of fixed monkey spinal cord showing maps of (A) axon diameter [μm] and (B) axonal density [1/μm2]. Largest axons can be observed in the corticospinal tract (CST) that is mainly responsible for motor function. Small and densely packed axons are observed in the sensory anterolateral (ALC) and dorsal column (DC).


T. Schneider, D.C. Alexander, and C.A.M. Wheeler-Kingshott, “Optimized diffusion MRI protocols for estimating axon diameter with known fibre orientation,” International society for magnetic resonance in medicine (ISMRM), Stockholm: 2010, p. 1561.

T. Schneider, C.A.M. Wheeler-Kingshott and D.C. Alexander . In-vivo estimates of axonal characteristics using optimized diffusion MRI protocols for single fibre orientation. MICCAI: 2010, pp 623-30.

Our approach is to combine computer simulations, pre-clinical high-resolution MRI and clinical in-vivo human spinal cord studies to develop new ways of characterising spinal cord organisation.

With new treatment strategies on the horizon, the developed techniques can provide novel biomarkers for measuring therapy outcome and will be of great significance in monitoring the success of future clinical trials.

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Forward and Inverse Models for Optical Tomgraphy.

Martin Schweiger

I am senior research associate in the Centre for Medical Image Computing. I trained as a Physicist and carried out my first degree at the Ludwig-Maximilian Universitat Munchen. I obtained my PhD in December 1994 in the Department of Medical Physics and Bioengineering at UCL.

Numerical MethodsNumerical solutions to partial differential equations are required to solve many real-world problems. We have developed a finite element solver package that provides the building blocks for solving a variety of problems, including diffusion processes and linear elasticity problems. The package is implemented in an object oriented framework that is both versatile and efficient. It contains a variety of element classes, basis functions and solver methods. The hierarchical class structure of the numerical and FEM libraries allows easy extension with new element types or solver methods.

Inverse ProblemsThe solution of nonlinear inverse problems generally requires iterative approaches that find the parameters of a differential equation by minimising a norm of the data difference. Optimisation stategies include methods that make use of the first derivative of the forward model (e.g. nonlinear steepest descent or conjugate gradient methods), the second derivative (e.g. Gauss-Newton, truncated Newton or Levenberg-Marquardt methods). Other methods use a statistical approach to describe the solution by the mean and standard deviation of a distribution of samples (Markov Chain Monte Carlo method).

Diffuse Optical TomographyThe numerical, modelling and inversion techniques described above are all applied to the problem of image reconstruction in diffuse optical tomography. Optical tomography is a medical imaging modality that seeks to recover the optical parameters of tissue from boundary measurements of infrared light transmission. The applications are in imaging of brain and muscle activity, monitoring of oxygen uptake and consumption, and breast tumour screening. Infrared light is strongly scattered in most biological tissues, making the propagation a

Reconstruction of homogeneous parameters in a slab. Geometry using a Markov-Chain Monte Carlo approach and a non linear conjugate gradient approach.


M. Schweiger, I. Nissila, D. A. Boas and S. R. Arridge, Image reconstruction in optical tomography in the presence of coupling errors, Appl. Opt. 46(14), 2743-2756 (2007).

S. Wright, M. Schweiger and S. R. Arridge, Reconstruction in optical tomography using the PN approximations, Meas. Sci. Technol. 18(1), 79-86 (2007).

M. Schweiger, O. Dorn and S. R. Arridge, 3-D shape and contrast reconstruction in optical tomography with level sets, in Applied Inverse Problems 2007: Theoretical and Computational Aspects, J. Phys.: Conf. Ser. 124, paper 012043 (2008).

M. Schweiger, O. Dorn, A. Zacharopoulos, I. Nissila and S. R. Arridge, 3D level set reconstruction of model and experimental data in Diffuse Optical Tomography, Optics Express 18(1), 150-164 (2010).

highly nonlinear process, and the reconstruction is an ill-posed inverse problem.

Toast Modelling and Reconstruction Package The finite element solver routines, as well as its application to the problem of image reconstruction in optical tomography, have been integrated into a complete imaging suite. The suite contains the core numerical and FEM libraries, application programs for the solution of the forward and inverse problems, and an interface to Matlab that allows users to rapidly build modelling and reconstruction scripts tailored to their specific applications. The TOAST software suite is published online and can be downloaded under http://web4.cs.ucl.ac.uk/research/vis/toast/

Genetic Basis of Neurological Disease

Benjamin Sinclair

I am currently undertaking a PhD jointly between CABI and CMIC, UCL. I am interested in researching methods to elucidate the underlying genetic features of neurological diseases using a combination of imaging techniques, genetics and physics.

Efforts are underway to create mouse knock-out models for all of the approximately 25,000 mouse genes, primarily in order to determine gene function and mimic human disease. CMIC, in collaboration with the Centre for Advanced Biomedical Imaging(CABI) are implementing and developing techniques to detect the morphological characteristics of genetically modified mice. High throughput magnetic resonance imaging of mouse strains is undertaken at CABI, and automated analysis of the resulting data sets to detect the neurological consequences of a gene manipulation is highly desirable.

My masters project involved the analysis of MRI data acquired at CABI’s 9.4T MRI machine from a mouse model of Down Syndrome, not a gene knock-out model, but rather an aneuploid mouse strain containing an additional

This figure shows a mouse brain atlas which is an average of the groupwise registered Down Syndrome and healthy control mice images, and overlaid in red/blue are the regions in which the size of a structure as a proportion of total brain volume reduces/increases in the Down Syndrome mouse model.


Automated tensor based morphometry for phenotyping the Tc1 mouse model of Down syndrome

B. Sinclair, J.O. Cleary, M. Modat, F. C. Norris, F.Wiseman, E.Fisher, M.F. Lythgoe and S. Ourselin

Proc BCISMRM 2010

copy of chromosome 21.

To establish the neurological characteristics of this mouse strain, statistical parametric maps are created to highlight regional differences in volume and gray matter density between the mouse strain under consideration and healthy controls. First group-wise registration using the NiftiReg package developed by Marc Modat of CMIC is carried out to establish structural correspondence between the mouse brains. Subsequently the SPM software package developed at UCL’s Institute of Neurology is used perform statistical comparison of the Down Syndrome model and healthy controls at every voxel of the resulting registered images. This produces a statistical parametric map which highlights regions of difference between the two mouse groups.

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Novel MRI Techniques for the Determination of Tissue Microstructural Parameters: Development, Implementation and Validation Invitro and InvivoBernard Siow

I’m a post-doc working as part of Daniel Alexander’s Microstructure Imaging Group (MIG) and I have very close links with the UCL Centre for Advanced Biomedical Imaging (CABI). I am a MRI physicist, and primarily, my work bridges together in silico techniques developed at MIG with implementation and data acquisition using MRI scanners at CABI,

Recent MRI advances have been used to non-invasively estimate tissue microstructural parameters. The length scale of tissue microstructural parameters is generally one thousand times smaller than conventional anatomical MRI scans. This project bridges novel MRI techniques that are sensitive to tissue microstructure parameters that are being developed in silico with the development, implantation and validation of these techniques in vitro and in vivo. The following techniques have been used to estimate tissue microstructure:

Investigating MRI techniques that are sensitive to the diffusion of water molecules: (i) Implementation and validation of diffusion MRI scans that use novel gradient waveforms. Applications include the estimation of axon radius distribution in neuronal diseases such as autism and schizophrenia. (ii) Optimisation of diffusion MRI scans for the detection of microstructural changes in prion diseases.

Figures: (a) Simulated signal for various axo radii using novel gradient waveforms i difusion MRI scans: show


1. B. Siow, I. Drobnjak, M. Lythgoe and D.C. Alexander. “Parameterised Optimised Gradient Waveform Spin-Echo sequence for Diffusion Weighted MR in a Microstructure Phantom” BC-ISMRM 2010. Ab-stract O13

2. J. O. Cleary, B. Siow, N. D. E. Greene, P. Daga, M. Modat, R. J. Ordidge, S. Ourselin, D.C. Alexander, M. F. Lythgoe. “Contrast-enhanced micro-Diffusion Tensor Imaging for Mouse Embryo Phenotyping” BC-ISMRM 2010. Abstract O26

3. B Siow, A. Chen, G. Clowry, L. Sun, A. Blamire. “Crushed Early Acquisition Spin Echo (CEASE): a novel technique for positive contrast and spectroscopic imaging of superparamagnetic particles” ISMRM 2009 Abstract 2072

4. B. Siow, D. W. Carmichael, J. Riegler, D. Alexander1 M. Lythgoe, and R. Ordidge. “Detection of Human Mononuclear Cells Labelled with Micron-Sized Iron Oxide Particles Using the Sub-Pixel Enhancement of Nonuniform Tissue (SPENT) Sequence” ISMRM 2010 Abstract 4942

Investigating MRI contrast agents to reduce scan times for micro-MRI and to track cells: (i) Optimisation of MRI scans and concentration of contrast agents for the detection of microstructural changes in spina bifida and necrotising enterocolitis. (ii) Development of novel MRI scans to track the migration of stems cells to sites of neuronal damage.

Investigating MRI techniques that are sensitive to the dipolar interactions between molecules of water: Development and optimisation of MRI scans to detect the degradation of articular cartilage in osteoarthritis.

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Rapid Phase-Contrast Magnetic Resonance Imaging Using Spiral Trajectories and Parallel Imaging

Jennifer Steeden

I am a final year PhD student working in the Centre for Medical Image Computing & based at the Institute of Child Health with the Cardio Thoracic Group.

We have developed a sequence based on an undersampled spiral acquisition and showed its use on free-breathing subjects undergoing exercise during the scan. We have subsequently developed a new acquisition scheme that, through intelligent choice of reference data, allows improvements in spatial or temporal resolution (see graph).

Translation of our work to clinical use has been aided by implementation of both the sequences and the reconstruction directly on the MR scanner. This enables seamless use and easy porting to other scanners. The sequence has been used for physical exercise studies in the scanner (see figure), mental stress studies and on patients for whom conventional imaging fails.


Steeden, J. A., Atkinson, D., Taylor, A. M., Muthurangu, V. Split-acquisition real-time CINE phase-contrast MR flow measurements. Magnetic Resonance in Medicine [Accepted]

Steeden, J. A., Atkinson, D., Taylor, A. M., Muthurangu, V. (2010). Assessing the hemodynamic response to exercise - A novel MR approach. Journal of Cardiovascular Magnetic Resonance. ( Vol. 12(Suppl 1) pp.O67-).

Steeden, J. A., Atkinson, D., Taylor, A. M., Muthurangu, V. (2010). Assessing vascular response to exercise using a combination of real-time spiral phase contrast MR and noninvasive blood pressure measurements. JOURNAL OF MAGNETIC RESONANCE IMAGING 31(4), 997-1003.

Steeden, J. A., Atkinson, D., Taylor, A., Muthurangu, V. (2009). Real-time Flow Measurements for the Assessment of Hemodynamic Response to Exercise. ISMRM Workshop on Cardiovascular Flow, Function and Tissue Mechanics

Steeden (nee Edgar), J., Muthurangu, V., Taylor, A., Atkinson, D. (2009). Undersampled Spirals for Real-time Flow Measurements. ISMRM 2009. ( pp.1855).

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Image Guided Laparoscopic Surgery

Stephen Thompson

My focus is on enabling image guidance for laparoscopic surgery. In particular, I am developing methods for overlaying CT and MRI images with the surgeon’s view to improve surgical decision making in the operating theatre.

The use of CT, MRI and ultrasound to locate and diagnose cancer, and to plan surgical procedures is now routine practice. In laparoscopic (keyhole) cancer surgery the surgeon usually has a good knowledge of the location and extent of the cancer prior to commencing surgery. Once surgery commences, however, it becomes difficult to refer back to the preoperative images in an intuitive way.

The aim of my research is to develop image guidance systems that enable preoperative images to be overlaid on, and aligned with, the patient’s visible anatomy.

To date, we have implemented a prototype system for laparoscopic radical prostatectomy. This has been trialled on 5 patients with promising results, enabling the surgeon to interact with the preoperative (MRI) images during the procedure. This has resulted in improved communication and decision making within the surgical team. Importantly, the system is entirely non-invasive, so the patient is unaffected by its introduction.

The image guidance system tracks both the patient and the endoscope using three infrared cameras (Optotrak Certus, NDI). We have identified accurate tracking of the endoscope using such devices as a key technological barrier to the utility of the system. Therefore, improving the accuracy of endoscope tracking forms the basis of my current research.


S. Thompson , G. Penney , D. Buie , P. Dasgupta , D. Hawkes “Use of a CT statistical deformation model for multi-modal pelvic bone segmentation.” Proceedings of the SPIE Medical Imaging 2008:

S. Thompson, G. Penney, T. Carter, P. Dasgupta, D. Hawkes “Accuracy Analysis of an Image Guided Robotic Urology Surgery System” Presented at Workshop on Geometric Accuracy in Image Guided Intervention, MICCAI 2009.

The tracking camera’s in the operating theatre. The endoscope is tracked using a set of infrared emitting diodes attached to a collar.

A patient’s MRI image overlaid onto the surgical scene. Suspected cancerous tissue in the peripheral zone of the prostate is circled in yellow.

A patient’s MRI image overlaid onto the surgical scene. Suspected cancerous tissue in the peripheral zone of the prostate is circled in yellow.

I am also in the process of applying the image guidance system to laparoscopic liver surgery, as a part the European PASSPORT project.

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Iteratively Reconstruction and Registration for Digital Breast Tomosynthesis

Guang Yang

In my PhD work I am investigating combining the reconstruction and registration for Digital Breast Tomosynthesis (DBT), which is an X-ray modality that a small number of low dose X-ray images (typically between 10 and 50) are acquired over a limited angle and reconstructed into a 3D volume.

Digital Breast Tomosynthesis (DBT) has the potential to enhance breast cancer detection by reducing the confounding effect of superimposed tissue associated with conventional mammography. In addition the increased volumetric information should enable temporal datasets to be more accurately compared, a task that radiologists routinely apply to conventional mammograms to detect the changes associated with malignancy.

A key issue in the creation of DBT images is the algorithm used to perform the reconstruction. This has been a topic of substantial research with many algorithms being proposed.

In this research we address the problem of comparing DBT data by combining reconstruction of a pair of temporal volumes with their registration. This is a challenging task due to the significant artefacts associated with DBT reconstructions. These are generated by the limited field of view of the acquired images and the correspondingly large null-space in the frequency domain. Rather than registering the images after reconstruction therefore, we investigate the benefits of combining both reconstruction and registration, and test the hypothesis that the performance of each task will be enhanced as a result.

We propose an iterative method of least squares optimisation for our combined reconstruction and registration scheme. This avoids the implicit assumption of missing data being equal to zero in algorithms such as in FBP.


Yang G, Hipwell JH, Clarkson MJ, Tanner C, Mertzanidou T, Gunn S, Ourselin A, Hawkes DJ, Arridge SA. Combined Reconstruction and Registration of Digital Breast Tomosynthesis, Int. Workshop on Digital Mammography, 2010.

Yang G, Hipwell JH, Clarkson MJ, Tanner C, Mertzanidou T, Gunn S, Ourselin A, Hawkes DJ, Arridge SA. Combined Reconstruction and Registration of Digital Breast Tomosynthesis: Sequential Method versus Iterative Method. Medical Image Understanding and Analysis, University of Warwick, Coventry.

(a) Original test volume; (e)Transformed test volume; Sequential results; (b)-(d): (b) Reconstruction x1, (c) Reconstruction x2, and (d) Transformed reconstruction; Iterative results (f )-(h): (f )Reconstruction x1, (g) Reconstruction x2, and (h) Transformed reconstruction.

Using a simple test object, and DBT simulations from in vivo breast compressions imaged using MRI, we demonstrate that this combined reconstruction and registration approach produces improvements in both the reconstructed volumes and the estimated transformation parameters when compared to performing the tasks sequentially.

In future work we will investigate alternative non-rigid transformations and address the issue of change in the breast tissue which may occur between time points. Additionally, simultaneous schemes for reconstruction and registration of DBT are under developing.

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Diagnosis and Prediction of Alzheimer’s Disease from Structural MRI

Jonathan Young

My research makes use of structural MRI image data to classify patients with dementia using high-dimensional pattern classification approaches, the eventual goal being to successfully predict the onset of Alzheimer’s before symptoms are present.

There has been a great deal of recent publicity about the diagnosis of Autism by making use of structural MRI brain scans and pattern recognition, but similar approaches have been used to diagnose Alzheimer’s disease for several years.

Alzheimer’s causes atrophy in many brain structures, causing an enlargement of the ventricles and shrinkage in the hippocampus. Initial attempts to take advantage of this used a single measurement per patient, the hippocampal volume, to diagnose disease.

However rates of atrophy are not uniform across the hippocampus and so it tends to change in shape as well as size as the disease progresses. This offers the possibility of more accurate diagnosis as there are many more dimensions across which sample images can be separated.


J. Young. 2010. High dimensional pattern classification of Alzheimer’s disease and MCI using hippocampal shape. Master of Research Thesis, University College London.

A software package is used to parameterise the shape of each sample segmented hippocampus (figure 1) – for example SPHARM which represents the shapes as weighted sums of three dimensional spherical harmonic functions.

These representations are then used to construct a Support Vector Machine (SVM), which treats the set of weights from the SPHARM representation of each hippocampus as a point in a very high-dimensional space and then calculates the hyperplane optimally separating diseased and control hippocampi. This can then be used to classify unseen hippocampi based on which side of the plane they fall. When the correct sets of coefficients and parameters tuning the SVM are selected this method can classify hippocampi with over 84% overall accuracy and 95% confidence interval for this is [79.4%, 88%]. (figure 2).

Further improvements may come from using images of the whole brain and from manifold learning techniques that treat the data as lying on a nonlinear manifold embedded within the high-dimensional space.

Figure 1: A typical segmented hippocampus

Figure 2: Overall classification accuracy as a function of SVM cost C and kernel radius γ

Investigating White Matter in Health and Disease, from Macro to Micro

Gary Hui Zhang

My interest is in developing computational techniques for diffusion MRI and the application of such techniques to imaging white matter microstructure and mapping brain connectivity.

Population-based studies underpin our growing understanding of brain connectivity during normal development and aging as well as disease. I have made important technical contributions that optimise such studies for diffusion MRI, including novel algorithms for spatial normalization of diffusion MRI data, optimal construction of white matter atlas, and tract specific analysis of white matter morphometry.

The focus of my current research is the direct measurement of white matter microstructure using diffusion MRI. The ultimate aim of this effort is to realize non-invasive mapping of both white matter connectivity and tissue microstructure parameters, such as axon diameter and density, over the whole of living human brain on a clinical scanner.

I continue to emphasize the translational aspect of my research. I lead the DTI-TK project (http://www.nitrc.org/projects/dtitk) that disseminates diffusion MRI optimized algorithms for population based studies as a freely available software package, and maintain collaboration on various clinical applications of these technical advances, such as autism, fronto-temporal dementia and motor neuron disease.


H Zhang, P A . Yushkevich, D C Alexander, and J C Gee. Deformable registration of diffusion tensor MR images with explicit orientation optimization. Medical Image Analysis, 10(5):764-785, 2006.

H Zhang, B B Avants, P A Yushkevich, et al, High-dimensional spatial normalization of diffusion tensor images improves the detection of white matter differnces in amyotrophic lateral sclerosis. IEEE Transactions on Medical Imaging 26(11):1585-1597, 2007.

P A Yushkevich, H Zhang, T J Simon, and J C Gee. Strucure-specific statistical mapping of white matter tracts. Neuroimage, 41(2):448-461, June 2008.

H Zhang, S P Awate, S R Das, et al. A tract-specific framework for white matter morphometry combining macroscopic and microscopic tract features. Medical Image Analysis, 14(5):666-673, 2010.

H Zhang, D C alexander, Axon diameter mapping in the presence of orientation dispersion with diffusion MRI. In International Conference on Medical Image Computing and Comuter Assisted Intervention (MICCAI), Part I, LNCS 6361 September 2010.

The pipeline for population based studies of white matter implemented in DTI-TK. Image

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In peripheral white matter, axon diameters can be estimated more accurately with Watson model than with Delta.

Image Registration and Applications in Cardiology

Xiahai Zhuang

I am interested in medical image computing (registration and segmentation) and the clinical applications such as in cardiology.

Segmentation extracts volume and shape of anatomical regions, while registration provides spatial correspondence between different images. The computation can be an essential step for the development of many clinical applications. Heart disease is the leading cause of death and accounts for 30% of global deaths. Medical imaging and image computing have been widely used in clinical routine and shown tremendous potential for reducing this death toll by providing early diagnosis and treatment.

My research includes developing whole heart segmentation techniques. These techniques provide the segmentation of ventricles, myocardium, atria, and sometimes great vessels of the heart if they are of interests. To achieve a fully automated process, I have developed an atlas propagation based method using image registration techniques. This method employs several newly developed algorithms, as Fig.1 shows. The segmentation tool has been validated using an MR data set involving thirty-seven patients and nine types of pathologies. The segmentation framework also demonstrates potential of being applicable to cardiac CT and ultrasound. The technology can be essential for studying change of cardiac functions. It can enable early detection of cardiac disease symptoms in clinical diagnosis and to more accurately measure and understand the relations between cardiac outward features and special diseases/ treatments in clinical researches.

I am particularly interested in the research of image registration, including the techniques to improve the robustness against low quality images and large shape variation, the flexibility of the deformation model which determines accuracy, and maintain diffeomorphism.

I have developed the Locally Affine Registration Method (LARM). LARM

assigns a local affine transformation to each substructure to locally maintain the affinity or rigidity, while globally it is a deformable registration, as Fig.3 shows. It is an attractive alternative for some applications where a global affine transformation cannot provide enough accuracy while a non-rigid registration would affect incorrectly the local topology.

I have been studying the techniques of spatial information encoding and the application to image registration. Traditional mutual information (MI) registration may be inappropriate in some applications when the images have intensity non-uniformity or enhancement by contrast agents. Spatially encoded mutual information (SEMI/SIEMI) includes the spatial information into MI registration to tackle this problem. It has demonstrated promising results in several applications, including brain MRI (Fig.5), cardiac MRI, and contrast enhanced MRI of the liver.


Zhuang, et al.: A Registration-Based Propagation Framework for Automatic Whole Heart Segmentation of Cardiac MRI. IEEE Trans. Med. Imag. (in press)

Zhuang, et al.: Whole Heart Segmentation of Cardiac MRI Using Multiple Path Propagation Strategy. MICCAI’10 (in press)

Zhuang, et al.: Unifying encoding of spatial information in mutual information for nonrigid registration. IPMI’09, 491-502

Zhuang, et al.: An atlas-based segmentation propagation framework using locally affine registration – Application to automatic whole heart segmentation. MICCAI’08, 425-43

Fig. 1 whole heart segmentation framework

Fig. 2 whole heart anatomy and quantification

Fig. 3 Locally affine registration method

Fig. 4 Illustration of spatial encoding

Fig. 5 by MI by SIEMI

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Funders

The Centre for Medical Image Computing would like to thank all our funding partners who make our research possible:

Research Councils and National/International Funders

EPSRC MRC Cancer Research UK Department of Health European Commission FP7 National Institute for Health Research NIH Royal Academy of Engineering Technology Strategy Board

Charities

SPARKS Wellcome Trust

Industrial Partners

Dexela Ltd GE Healthcare GlaxoSmithKline Ixico Ltd Medicsight Ltd Mirada Medical Ltd Philips Research Hamburg Siemens VisionRT Ltd

For a full list of publications please visit :www.ucl.ac.uk//cmic

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Notes

Centre for Medical Image Computing

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