MASTER THESIS TOPICS - MEDISIPmedisip.ugent.be/wp-content/uploads/2019/04/Thesis-topics-present… · master thesis topics academic year 2019-2020 department of electronics and information

MASTER THESIS TOPICSAcademic year 2019-2020

DEPARTMENT OF ELECTRONICS AND INFORMATION SYSTEMS (ELIS)

MEDICAL IMAGE AND SIGNAL PROCESSING (MEDISIP)

MEDISIP

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MEDISIP

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RESEARCH GOALS OF MEDISIP

• Make medical imaging more quantitative

• Improve acquisitions/reconstructions

i. Reduce imaging time

ii. Improve spatial resolution

• Solve artefacts in multimodal integration

• Additional information from multimodal data

• Application fields: small animal and neuroimaging

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RESEARCH ACTIVITIES @ MEDISIP

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RESEARCH ACTIVITIES @ MEDISIP

ONGOING PHD PROJECTS

• Radiomics-machine learning-brain tumors (partner nuclear medicine/radiology)

• PET imaging in plants (partner Bioengineering)

• PET-MRI novel isotopes (partner KULeuven)

• Dosimetry in radionuclide therapy (Lutetium, partner Bordet)

• High resolution detectors for Total body PET

• Monolithic Time-of-flight detectors for PET

Collaborations

• EEG/Epilepsy with Neurology dept

• Intraoperative PET/CT lumpectomy margin assessment (R. Van den Broucke)

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IMAGING

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F-18 LABELING OF MICROSPHERES TO ENABLE INTERVENTIONAL PET FOR MINIMALLY INVASIVE LIVER RADIO-EMBOLISATION

Supervisor: Marek Beliš, Ken Kersemans (UZ Gent)

Promotors: prof. Stefaan Vandenberghe, prof. Christian Vanhove

Background

Targeted radionuclide therapy (TRT) is an established cancer treatment modality. It relies

on cancer specific agents that are labeled with radionuclides for internal radiotherapy. By

the use of disease specific carriers linked to radionuclides emitting particle with a short

range, a high dose of radiation can be delivered to tumors while sparing the unaffected

organs. Imaging the distribution of these radionuclides is required for individual

assessment and planning of TRT.

When we would have theranostic F-18 labeled spheres PET imaging could be used to

combine diagnostic and therapeutic procedures in one procedure. For this reason we

want to study three radiolabelling strategies to introduce PET isotopes (F-18) onto the

surface of the microparticles

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Goal

• Investigate the different labeling options.

• Image the labeled microspheres with a high-resolution PET system

(available at Infinity lab).

An optional area of research is to investigate with flow simulations the flow

of the microspheres in a typical hepatic artery and liver.

Tools: Modeling, hotlab, PET …

Remark: this project is of direct interest from a pharma company delivering

therapeutic microspheres

Timeline: literature study, (simulation), labeling, data analysis

More information?! 📩 [email protected]

HIGH SENSITIVITY SPECT USING 12 ROTATING PARALLEL COLLIMATED DETECTORSSupervisor: Marek Beliš, Dr. Bieke Lamber (UZ Gent)

Promotors: prof. Stefaan Vandenberghe, prof. Roel Van Holen

Background

SPECT is the most frequently used techniques and detects single photon

emittors by a mechanical collimator and scintillation detector. The

conventional gamma camera, based on a 40-year old design, is

composed of 2 large (about 40-50 cm) detector heads equipped with large

parallel hole collimators. This limits the sensitivity and spatial resolution of

SPECT imaging. To obtain relevant images, relative long acquisition times

and/or high doses are required. A totally new design based on 12 detector

(CZT) heads has been recently commercialised and first systems are

installed at 4 clinical sites (France). Each head has an axial dimension of

35 cm and a smaller axial dimension of about 5 cm. These detectors can

be brought very close to any body part of the patient to improve spatial

resolution. For small objects also a larger sensitivity can be obtained.

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Goal

The aim of this thesis is to characterize in detail how much improvement can be expected from such a design in

typical imaging situations

Tools: Literature, Monte Carlo simulations, MATLAB, SPECT, …

Remark: First 4 systems are installed at sites in France

measurements can be performed on these sites

Timeline: literature study, simulations, data analysis

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INVESTIGATION OF LYSO BACKGROUND RADIATION IN A TOTAL-BODY PET

Supervisor: Charlotte Thyssen


Background

Positron Emission Tomography (PET) is a molecular imaging modality that

uses a radioactive tracer to visualize processes occurring inside the body.

However, conventional systems only have a very small length → a lot of the

radiation produced inside the patient is lost …

For this reason MEDISIP wants to develop a total-body PET with a length of 1

meter → ~20x more radiation is caught!!

LYSO, the scintillator crystal of choice, is naturally radioactive → background

radiation present during scanning

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Total-body PET system

Conventional PET system

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Goal

• Mapping out the effect of background radiation in total-body PET

• Monte Carlo simulations of human phantoms with and without

background in total-body PET

Software: Gate, XCAT, MATLAB/Python, Root, …

Timeline: literature study, Monte Carlo simulations, image

reconstruction, data analysis

MEDIUM-SIZE ANIMAL PET SCANNER: INVESTIGATION OF IDEAL SCANNER GEOMETRY

Supervisor: Charlotte Thyssen


Background

Today, rats and mice are mostly used for scientific research, however, translation

of the obtained results to humans is not straightforward. For this reason there is

an increased interest in larger animals like rabbits. Preclinical imaging modalities

for these animals are scarce. The idea is to increase the bore size of the

MOLECUBES PET-scanner and to include TOF capabilities.

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Goal

• Comparison of different designs for medium-size animal scanners

• Effect of TOF inclusion in medium size animal scanners

• Comparison of different scintillation crystals to reduce cost

Software: Gate, XCAT, MATLAB/Python, Root, …

Timeline: literature study, Monte Carlo simulations, image

reconstruction, data analysis


ACCELERATING MONTE CARLO SIMULATIONS FOR MEDICAL SCANNER DATA WITH JULIA

Supervisor: Charlotte Thyssen, Tim Besard

Promotors: prof. Bjorn De Sutter, prof. Stefaan Vandenberghe

Background

Monte Carlo simulations are used for simulation of medical imaging data (to optimize

image reconstruction or simulate innovative system designs). The code is based on

the computationally intensive Geant 4 package (CERN). Simulation of realistic

patient data is a very slow process and needs to be run on multiple CPU or GPU, to

obtain data in an acceptable time frame (days/weeks). Acceleration of this code

would benefit a large community of researchers working on improved medical

imaging systems.

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Goal

• Identify the critical parts in the library

• Evaluation of the potential of Julia to make Monte Carlo simulations much more efficient and more easily

accessible

Software: Gate, Julia

Timeline: literature study, Monte Carlo simulations, analysis of simulation code and optimization using Julia

Two different types of simulations will be investigated: the first one relies on voxelized sources for determining

patient interactions (e.g., Dosimetry purposes) and the second is the scanner simulation part.

To reach these goals, we are looking for students with considerable programming experience and a passion for

the latest state-of-the-art programming languages.


HIGH-PERFORMANCE YET RAPID IMAGING RECONSTRUCTION WITH JULIA (1 OR 2 STUDENTS)

Supervisor: Charlotte Thyssen, Tim Besard

Promotors: prof. Bjorn De Sutter, prof. Stefaan Vandenberghe

Background

After image acquisition, recorded data are obtained as a list of

events or projection data sets. An image reconstruction algorithm

uses this output data from the scanner to calculate the 3D image

of the patient. This step is done in an iterative loop and typically

involves several matrix multiplications resulting in a

computationally intensive algorithm. The image reconstruction

needs to be run on multiple CPU or GPU to be able to keep it

equal to the faster acquisition of the most recent scanners.

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Reconstruction by back projection

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Goal

• Migrate the state-of-the-art medical image reconstruction code developed at MEDISIP

• Use Julia to answer the existing open questions

Software: QETIR, Julia

Timeline: literature study, image reconstruction, analysis of reconstruction code and optimization using Julia,

analysis of a second algorithm algorithm (even-based) and comparison to first

Possibility for collaboration with MOLECUBES (UGent Spin-off)

To reach these goals, we are looking for students with considerable programming experience and a passion for

the latest state-of-the-art programming languages.


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Deep Learning for Computer-Aided Detection and Diagnosis of Breast Cancer

Background

Breast cancer is the second leading cause of cancer-related

death among women

Early detection increases the chance of full recovery

Screening mammography is associated with a high risk of

false positive testing

Computer-aided detection and diagnosis (CAD) systems:

Supervisor: Milan Decuyper

Promotor: prof. Roel Van Holen

workload Accuracy+

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Goal

Design and train algorithms for computer-aided detection or

diagnosis of abnormalities in mammograms such as calcification and

masses.

Data: CBIS-DDSM database @ The Cancer Imaging Archive.

Software: Python (PyTorch/Tensorflow/Keras/...)

Different tasks possible such as:

- Detection of breast cancer

- Segmentation of masses and calcifications

- Diagnosis of masses and calcifications as benign or malignant

http://www.cancerimagingarchive.net/

Deep Learning for Computer-Aided Lung Nodule Detection

Background

Lung cancer is the leading cause of cancer-related death

worldwide

Early detection reduces lung cancer mortality

Manual interpretation of lung CT scans is error-prone and time

intensive.

Computer-aided detection and diagnosis (CAD) systems:

Supervisor: Milan Decuyper

Promotor: prof. Roel Van Holen

workload Accuracy+

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Goal

Design and train algorithms for computer-aided lung nodule annotation

in CT scans.

Data: LIDC-IDRI, NSCLC-Radiomics and NSCLC-Radiogenomics

@ The Cancer Imaging Archive.

Different tasks possible such as:

- Lung Nodule Detection

- Lung Cancer Diagnosis, survival prediction, prediction

of genomic mutations etc.

Software: Python (PyTorch/Tensorflow/Keras/...)

http://www.cancerimagingarchive.net/

FAST AND EFFICIENT RECONSTRUCTION ALGORITHM FOR MAGNETIC RESONANCE ELECTRICAL PROPERTIES TOMOGRAPHY (MREPT)

Supervisors: Prakash Parappurath Vasudevan

Promotors: prof. Roel Van Holen, prof. Wout Joseph

Background

MREPT is a technique used to obtain the admittivity (both conductivity and permittivity) of tissues

Electrical properties (EP) can be used for Cancer diagnosis, Staging and Grading

EPs are critical in applications utilizing EM stimulation for treatment

Accurate assessment of EPs are necessary for subject Specific Absorption Rate (SAR) measurements

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B1 Mapping Reconstruction

Conductivity/Permittivity Image

Goal

• Improve the existing reconstruction algorithm of MREPT

• Test the algorithm using Electromagnetic (EM) field simulation

• Optimize the algorithm for different measurement set-up

• Investigate different B1 mapping methods and compare their performance

Data: Simulated B1 maps, MRI data of Phantoms and Mouse tumour models

Software: MATLAB/Python, Sim4Life (optional)

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For more information: [email protected]

RHENIUM-188 SPECTRA SIMULATION FOR SPECT

Supervisor: Marek Beliš

Promotors: prof. Stefaan Vandenberghe

Background

Rhenium-188 (188Re)

• theranostic agent => β- and γ-emissions

• 155 keV γ-ray (15 %) suitable for SPECT => single-photon emission

computed tomography

• similar to 99mTc

• several high-energy γ-rays in emission spectrum & Bremsstrahlung

may complicate quantitative imaging

Collimation is necessary to ensure good reconstruction, therefore parameters

of the collimator affect the quality of images, but also sensitivity etc.

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Goal

• Simulation of 188Re spectra and comparison to 99mTc

• Search for improvement by changing the parameters of the

collimator

• Possible upgrade to more radionuclides

Software: Gate, MATLAB, …

Timeline: literature study, Monte Carlo simulations, data analysis,

3D-modelling

DEVELOPMENT OF LIGANDS FOR COMPLEXES WITH RHENIUM

Supervisor: Marek Beliš

Promotors: prof. Stefaan Vandenberghe

Background

Rhenium-188 (188Re)

• β- and γ-emissions => theranostic agent (suitable both for therapy and

imaging)

• chemically similar to Tc, but with much more complicated redox

chemistry

Stability of the radiopharmaceutical is the key aspect for success of targeted

radionuclide therapy (TRNT). Therefore development of ligands stabilizing the

Re is necessary.

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Radionuclide – 188Re, 99mTc

Biomolecule

Cancer

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Goal

• Synthetical modification of macrocyclic ligands

• Formation of complexes with cold Re and later with 188Re

• Radiolabelling of biomolecules, stability testing

Fields: Organic synthesis, coordination chemistry

Timeline: literature study, synthesis, coordination chemistry, data

analysis

Cooperation with SCK•CEN (Belgian Nuclear Research Centre)

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NEUROENGINEERING

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CALIBRATING EEG SOURCE IMAGING USING EVOKED RESPONSES

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Supervisor: Jolan Heyse

Promotors: prof. Pieter van Mierlo

Background

In EEG source imaging, the brain activity underlying the measured EEG is

estimated by modelling the spreading of electrical activity in MR-based

electromagnetic head models. Despite very accurate models that are available

these days, the spatial resolution of EEG source imaging is in the order of cm.

New MRI sequences (ultra-short echo time, UTE) could help to further improve

the head models by refining the tissue segmentation. Evoked potentials (e.g.

finger tapping) can be used to evaluate the performance of ESI with the new

head model.

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Goal

• To evaluate the spatial resolution of existing EEG source imaging

methods and improve it through refinement of the head model.

Parametrization of the head model and assessing the spatial

resolution of the EEG source imaging will be done using evoked

potentials as a ground truth.

• A lot of the work will be practical. The student will obtain his/her

own data (MRI and EEG) for further analysis.

Software: MATLAB/Python

Timeline: literature study, MRI/EEG experiments, data analysis


EEG SIGNALS TREATED AS SOUND


Promotors: prof. dr. ir. Pieter van Mierlo, prof. dr. ir. Nilesh Madhu

Background

Because signal transmission occurs instantaneously in the brain, each EEG

electrode measures the sum of the individual activities of different brain regions.

Individual contributions of the different sources can be obtained by applying a de-

mixing procedure. The EEG signals are further corrupted by different artifacts of

environmental (e.g. 50Hz hum from power supplies) and biological (e.g. eye

blinks, muscle activity etc.) nature. Similar problems have been well-studied for

the multi-microphone recording and processing of audio signals and robust

solutions have been developed for these use-cases.

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Goal

• To use algorithms developed for speech/audio processing (e.g.

beamforming and spectral corrections) to get more information from the

EEG signals.

• These techniques will be applied to the problem of localizing the

epileptic focus in epilepsy patients. Seizure recordings often involve

activity from many brain regions and contain many artifacts because of

muscle contraction and movement of the patient.


Timeline: literature study, algorithm implementation, data analysis


EEG SOURCE IMAGING AND FUNCTIONAL CONNECTIVITY ANALYSIS OF MONKEY EEGSupervisor: Jolan Heyse

Promotors: prof. dr. ir. Pieter van Mierlo

Background

In EEG source imaging, the brain activity underlying the measured EEG signals is

estimated. Looking at the activity patterns from different brain regions, functional

connectivity methods can be applied to reconstruct the functional network of the brain

(i.e. how do the brain regions interact with each other?). Many methods exist for

assessing functional connectivity, but they are hard to validate as the ground truth

communicating network is rarely known. A dataset of simultaneous recordings with

scalp and intracranial EEG with electrodes placed inside a monkey's brain can serve

as a validation tool.

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Goal

• To validate different functional connectivity methods, plus the added

value of time lag information, based on the monkey dataset.


Timeline: literature study, data analysis, functional connectivity evaluation


EEG-NEUROFEEDBACK FOR IMPROVED BCI PERFORMANCESupervisor: Jolan Heyse

Promotors: prof. Pieter van Mierlo

Background

Brain computer interfaces (BCI) involve direct communication between the brain and

an external device (e.g. a neuro-prosthetic limb). As EEG provides a direct

measurement of brain activity, it poses a viable candidate as communicating

interface in BCI. However classification of brain signals into the intended tasks is

hampered by the complexity and variability of the underlying activity. Neurofeedback

uses real-time displays of brain activity to teach self-regulation of brain function

and could help to improve BCI performance by teaching the subject to steer brain

activity towards the desired classification area.

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Goal

• To establish an EEG-based BCI and evaluate the added value of

neurofeedback on the performance, or to learn new tasks using

neurofeedback


Timeline: literature study, experiment design, neurofeedback and BCI

implementation, experiments, data analysis


AGE RELATED CHANGES IN CORTICO-CORTICAL CONNECTIONS IN PHONEME DISCRIMINATION


Promotors: prof. dr. ir. Pieter van Mierlo, prof. Miet De Letter

Background

Phonemes are perceptually distinct units of sound and can be considered

fundamental building blocks for speech. Discrimination of these phonemes is

important for speech comprehension and has been investigated in an EEG-study

conducted at Ghent University. In this study, aging was associated with

increased latencies and decreased amplitude with age during phonemic

discrimination tasks. However, why this difference was observed is not yet

explained.

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Goal

• To investigate the connection between brain regions during phoneme

discrimination tasks. Functional connectivity analysis will be used to

reveal information flow in several frequency bands.

• We will investigate these interactions during phoneme discrimination and

study age-related differences. This will shed light on why elderly have

more difficulties discriminating phonemes.


Timeline: literature study, data analysis, clinical interpretation


INTERHEMISPHERIC CONNECTIVITY OF SUBCORTICAL NUCLEI DURING WORD TASKSSupervisor: Jolan Heyse

Promotors: prof. dr. ir. Pieter van Mierlo en prof. dr. Patrick Santens

Background

Deep brain stimulation is an established treatment for patients with Parkinson’s

disease. Here depth electrodes are bilaterally implanted in the subthalamic

nucleus (STN). In literature it has been shown that the stimulation of the STN

has an impact on speech. However, the exact role of the STN during speech and

the coupling between the STNs from both hemispheres remains to be

elucidated.

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Goal

• To investigate the communication between the left and right STN during

word tasks. We will work with intracranial EEG data from patients with

implanted electrodes. The stimulator is only implanted a couple of days

after the depth electrodes, which provides us a time frame to measure

intracranial EEG activity.

• Several word tasks have been recorded in a number of patients, where

action and non-action words were visually shown to the patients.


Timeline: literature study, data analysis


SPIKE SORTING OF SUBTHALAMIC SINGLE NEURON RECORDINGS IN PARKINSON PATIENTS


Promotors: prof. dr. ir. Pieter van Mierlo en prof. dr. Patrick Santens

Background

Deep brain stimulation of the subthalamic nucleus (STN) is an established

treatment to reduce motor tremors in patients with Parkinson’s disease. A depth

electrode is implanted into the subthalamic nuclei to stimulate the neurons. First

multiple micro-electrodes are inserted into the STN, that are capable to record

multiple single neurons. Based on these recordings, the location is chosen to

implant the macro-electrode that is used for current stimulation. Because the

micro-electrode records the activity of multiple neurons simultaneously, spike

sorting algorithms are used to separate the activity of the neurons.

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Goal

• To investigate the micro-electrode recordings in patients with

Parkinson’s disease. This requires the implementation of spike

sorting algorithms which allow separating activity from the different

recorded neurons. Different algorithms will be implemented and

their performance will be assessed.

• Furthermore the relation between the micro-recordings of neuronal

activity and the macro-recordings of local field potentials will be

studied.


Timeline: literature study, data analysis, algorithm evaluation


AUTOMATED EPILEPSY DIAGNOSIS FROM ROUTINE EEG USING MACHINE LEARNING

Supervisor: ir. Tom Van Steenkiste, prof. dr. Dirk Deschrijver

Promotors: prof. dr. ir. Tom Dhaene, prof. dr. ir. Pieter van Mierlo

Background

Epilepsy is a neurological disorder that affects approximately 0.5-1% of the

world’s population. The most important technique to diagnose epilepsy is

electroencephalography (EEG). In the EEG, the occurrence of epileptic spikes,

i.e. brief electrical discharges in the brain, are a hallmark to diagnose epilepsy.

The occurrence of epileptic spikes differs from patient to patient and even within

a patient from time to time. In clinical practice, a routine EEG of 20min duration

is recorded to diagnose epilepsy. Unfortunately, many patients with epilepsy do

not have frequent spikes; therefore the sensitivity of routine EEG to confirm the

diagnosis of epilepsy is only 25-56% and the specificity is 78-98%.

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Goal

• To increase the sensitivity and specificity of routine EEG to

diagnose epilepsy. This will be done by using and developing state-

of-the-art machine learning techniques to classify routine EEGs

recorded in Ghent and Geneva University Hospital as epileptic or

non-epileptic.

• In addition to the detection of epilepsy, classification into subtypes

can be performed. In a first step, classification in temporal vs extra-

temporal lobe epilepsy can be done.

• This master thesis is in close collaboration with Epilog, a startup

company specialized in EEG analysis. The student has the

opportunity to do an internship at Epilog before the master thesis.


Timeline: literature study, data analysis, machine learning


DETECTING THE CAUSE OF DEMENTIA USING EEG MEASUREMENTS AND MACHINE LEARNINGSupervisor: ir. Tom Van Steenkiste, prof. dr. Dirk Deschrijver

Promotors: prof. dr. ir. Tom Dhaene and prof. dr. ir. Pieter van Mierlo

Background

Dementia is a syndrome of several diseases: Alzheimer’s Disease (AD),

Frontotemporal lobe degeneration (FTD), creutzfeldt-jakob disease (CJD) or

Lewy body disease (LBD). Up to now, there is no medical test to diagnose which

disease is causing the dementia. Some pilot studies have indicated that

electroencephalography (EEG) could be a useful neuroimaging technique to

diagnose the cause of dementia. At the same time, recent advancements in

machine learning and deep learning have resulted in powerful analysis

techniques for medical time-series data. The application of machine learning to

EEG data for detecting the cause of dementia could lead to valuable insights

and models and could optimize patient treatment.

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Goal

• To use machine learning to classify EEGs from patients that have

dementia into AD, FTD, CJD and LBD. A post-mortem confirmed

database from Antwerp University Hospital is available to address this

question. The student can explore and develop state-of-the-art machine

learning algorithms for time-series analysis and can develop custom

algorithms for EEG data analysis.

• This master thesis is in close collaboration with Epilog, a startup

company specialized in analyzing EEG data. The student has the

opportunity to do an internship at Epilog before the master thesis.




EFFECT OF ANTI-EPILEPTIC DRUGS ON FUNCTIONAL BRAIN CONNECTIONS


Promotors: prof. dr. ir. Pieter van Mierlo, dr. Gregor Strobbe

Background

The first line treatment of epilepsy is antiepileptic drugs (AEDs). In

approximately 60-70% of patients AED mono- or polytherapy have

the desired outcome, namely the patient is seizure-free. Most of the

AEDs go hand in hand with many side-effects such as drowsiness,

dizziness, fatigue, nausea and vomiting. In all patients an AED is

tested without knowing whether the AED will lead to seizure freedom

or not. Furthermore, the side effects cannot be predicted.

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Goal

• To assess how antiepileptic drugs affect the functional connectivity

and whether these alterations are indicative for the side-effects of

the AEDs.

• Furthermore we will investigate the possibility of predicting who will

be a drug responder (i.e. seizure-free) or not, based on the

functional connectivity.




SMALL ANIMALS

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USING FUNCTIONAL MRI AND GRAPH THEORY TO INVESTIGATE ABNORMAL FUNCTIONAL BRAIN NETWORKS IN A RAT MODEL OF TEMPORAL LOBE EPILEPSY

Supervisor: Emma Christiaen

Promotors: prof. Chris Vanhove, prof. Robrecht Raedt

Background

• Epilepsy is a disease characterized by recurrent seizures

• More insight into the functional brain networks involved can lead to new therapies

• Resting state functional magnetic resonance imaging (fMRI) can be used to identify functionally connected

brain regions and construct functional networks

• These networks can be analysed and compared using graph theory

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Goal

Use graph theory to investigate abnormal functional brain networks in a rat

model of temporal lobe epilepsy

- use previously acquired resting-state fMRI images

- preprocess images and do global signal regression

- construct networks of functionally connected brain regions

- analyse networks using graph theory

Software: Matlab

59More information?! 📩 [email protected]

USING RESTING STATE FUNCTIONAL MRI TO IDENTIFY QUASI-PERIODIC PATTERNS OF FUNCTIONAL CONNECTIVITY IN A RAT MODEL OF TEMPORAL LOBE EPILEPSY


Promotors: prof. Chris Vanhove, dr. Benedicte Descamps

Background

• Functional magnetic resonance imaging (fMRI) is a functional imaging technique that allows the visualization of

whole-brain activity


brain regions

• Functional connectivity is usually assumed to be stationary

• In reality it varies over time and recurring patterns can be found (=quasi-periodic patterns)

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Goal

Identify quasi-periodic patterns of functional connectivity using resting state

fMRI data of the rat brain and investigate how these patterns differ in healthy

and epileptic animals


- identify quasi-periodic patterns

- compare patterns between healthy and

epileptic animals

Software: Matlab

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DYNAMIC PET IMAGING OF CHEMOGENETIC MODULATION OF THE HIPPOCAMPUS


Promotors: prof. Chris Vanhove, dr. Benedicte Descamps

Background

• Chemogenetics is a neuromodulation technique that allows very specific activation or inhibition of neurons

• Neuronal activity can be modulated by injecting a drug-like ligand (clozapine)

• Dynamic PET imaging allows monitoring of radioactive tracer uptake over time

• Changing concentration of radioactivity in tissue gives information about underlying mechanisms of diseases or

interventions

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Goal

Use dynamic PET imaging to investigate the effects of chemogenetic

modulation of the hippocampus

- acquire dynamic 18F-FDG PET images of animals while clozapine is administered -> inhibition of hippocampus

- visualize changing concentration of radioactivity in brain regions

- visualize the effect of inhibition of hippocampus

Software: MATLAB, Amide, Amira


USING DIFFUSION MRI AND TRACTOGRAPHY TO INVESTIGATE CHANGES IN WHITE MATTER TRACTS IN A RAT MODEL OF TEMPORAL LOBE EPILEPSY



Background

Epilepsy is a disease characterized by recurrent seizures

Diffusion magnetic resonance imaging (dMRI) can be used to identify epileptogenic abnormalities

White matter tracts can be mapped using tractography

More insight into changes in white matter tracts during the development of epilepsy can lead to new

biomarkers or therapies

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Goal

Use dMRI and tractography to investigate abnormal white matter tracts in a rat model of temporal lobe epilepsy

- use previously acquired diffusion MRI images

- preprocess images and do tractography

- investigate changes in known white matter tracts

Software: MATLAB, ExploreDTI, MRtrix3


MACHINE LEARNING FOR DISEASE DIAGNOSIS AND PROGNOSIS IN A RAT MODEL OF TEMPORAL LOBE EPILEPSY

Supervisors: Emma Christiaen, Milan Decuyper


Background

• Epilepsy is a disease characterized by recurrent seizures

• Not clear which patients will develop epilepsy after head trauma

• Need for biomarkers: functional brain networks involved in development of epilepsy


brain regions and construct functional networks

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Goal

Use machine learning to identify epileptic rats and to predict their eventual seizure frequency

- rat model of temporal lobe epilepsy


- construct networks of functionally connected brain regions

- extract features and build a classifier

Software: MATLAB, Python


ARTIFICIAL INTELLIGENCE FOR AUTOMATIC SEIZURE DETECTION IN EPILEPSY

Supervisor: dr. Lars Emil Larsen

Promotors: dr. Lars Emil Larsen and prof. dr. ir. Pieter van Mierlo

Background

Automatic seizure detection algorithms

• preclinical experiments: save experiments countless hours

• assist clinicians inspecting electroencephalographic data from epilepsy patients

• feedback driven closed-loop neurostimulation techniques for epilepsy

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Goal

• Electroencephalographic data will be available from several rodent epilepsy models, which will

be used to build seizure detection algorithms and compare performance.

• The project will revolve around testing the robustness of select machine learning techniques

such as random forest classification, support vector machines or neural networks.


Timeline: literature study,


ARTIFICIAL INTELLIGENCE FOR AUTOMATIC DETECTION OF HIGH FREQUENCY OSCILLATIONS IN EPILEPSY


Promotors: dr. Lars Emil Larsen and prof. dr. ir. Pieter van Mierlo

Background

Pathological high frequency oscillations (pHFOs)

• hallmark of epileptogenic brain regions

• reflect activity of a diseased brain predisposed to generate epileptic seizures

• exact mechanisms underlying pHFOs are unknown

• their frequency is generally correlated to seizure frequency

pHFOs are more frequent than seizures -> useful surrogate biomarker of disease severity

Quantification of pHFOs can be very labor intensive -> need for automatic detection tool

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Goal

• Electroencephalographic data will be available from

several rodent epilepsy models, which will be used to build

pHFO detection algorithms and compare performance.

• The project will revolve around testing the robustness of

select machine learning techniques such as random forest

classification, support vector machines or neural networks.


Timeline: literature study,


Example of HFO recorded in the epileptic hippocampus:

a) raw signal, b) high-pass filtered signal, c) relative time frequency plot

EEG CAP DEVELOPMENT FOR SMALL ANIMALS


Promotors: prof. dr. ir. Pieter van Mierlo and prof. dr. Robrecht Raedt

Background

• Preclinical validation of medical research on laboratory animals: rat brain model for human brain

• Humans: electroencephalography (EEG) using scalp electrodes

• Rats: small scalp area to place electrodes on => intracranial EEG (electrodes implanted in brain)

• Interesting to capture brain signals of rats from scalp electrodes -> a means to validate methods developed

for human scalp EEG

• Up to now, examples of scalp EEG for rats in literature are limited

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Goal

• Improve this prototype and to eliminate some existing problems. You can work on different aspects, depending

on what you prefer:

✓ How can we make the setup more practical? What is an easy, safe and fast way to fasten the cap to the rat’s head?

✓ Skin-electrode impedance could be lowered in order to better pick up the brain signals.

✓ What is the best technique and design for the electrodes?

✓ Can we improve the impedance with conductive gel?

✓ Techniques could be designed and implemented to shield the electrodes and cables from interfering signals, especially in

MR room.✓ Different materials for the electrodes to improve on MRI compatibility (making the artifact on the MR images smaller).

• Design and implement your improvements or make your own prototype. Finally, the system should be tested

and you will be able to register EEG data of rats


Timeline: literature study, experiments, data analysis


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Don’t hesitate to contact us for more information!

The presentation will be made available

on medisip.ugent.be

Documents

MASTER THESIS TOPICS - MEDISIPmedisip.ugent.be/wp-content/uploads/2019/04/Thesis-topics-present… · master thesis topics academic year 2019-2020 department of electronics and information