Competitive Analysis of CT Vendor Technologies · Competitive Analysis of CT Vendor Technologies ... So perhaps they are interested in that topic. ... They also study ways to improve

EST Competitive Technical Analysis

1 Steve Axelrod

Competitive Analysis of CT Vendor Technologies

Table of Contents

Major CT Vendors 2 GE Medical 2 Hitachi 3 Philips 4 Siemens 5 Toshiba (Aquilion product line) 6

Other companies in the CT reconstruction market 9 Medic Vision 9 TeraRecon 9 PreXion 9 Sirona 10

Appendix A – Competitive Reconstruction and Related Technologies 11

Appendix B – Glossary of Acronyms 15

Appendix C – Publications by the major CT vendors 17 GE Medical Publications 17 Hitachi Publications 19 Philips Publications 20 Siemens Publications 21 Toshiba Publications 23


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Major CT Vendors

GE Medical

ASIR (Adaptive Statistical Iterative Reconstruction) and ASIR-HD 40 ! 50% DR on the Discovery CT750 HD. Claim improvements in spatial resolution of 33%, low contrast detectability of 40%, and noise reduction(“subtracts rather than masks noise”) of 50%, and artifact reduction from beam hardening, calcium blooming and aliasing. ASIR can be used in a fractional sense, mixed with FBP. Researchers report optimal results using 40% and 60% ASIR rather than 100%. Significant number of independent publications exist.

SnapShot pulse technology Reduces cardiac dose up to 83% (presumably uses X-ray gating during the beating cycle for much of the reduction).

Models offered Discovery CT750 HD - ASIR reconstruction with up to 50% dose reduction, up to 128 slices, new X-ray source, new detector materials (Gemstone), spectral imaging with single source. LightSpeed series - ASIR reconstruction with up to 40% less dose, SnapShot Pulse for cardiac provides up to 83% dose reduction (presumably by gating), 16 slices, 40 mm coverage, 80 mm with VolumeShuttle option. BrightSpeed Series - compact, entry level multi-detector, 8 and 16 slice models. HiSpeed Series - Dual (2 slices) and CT/e (1 slice).

Summary of products ASIR has been tested by many independent researchers in various configurations (percent usage of ASIR). It appears to make a real difference, on the order of up to 40 - 50% dose reduction at its best. They appear behind in models with high number of slices, but have a wealth of other hardware innovations. They could be interested in EST as a modest improvement, but have a significant investment now in ASIR.

Publication summary (see list of publications in Appendix C) GE supports a great deal of research, including much in the last few years, perhaps the most of all the CT vendors. The key areas they work in are dose reduction by control of the X-ray and scanning parameters, various flavors of iterative and ART reconstruction (SART, parallel ART), GPU based reconstruction calculations, cardiac imaging in real time, and various approaches to cone beam reconstruction usually based on


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FBP/Feldkamp to reduce artifacts and improve coverage and speed. Their proprietary algorithm for dose reduction is ASIR, a classical iterative algorithm using FBP and forward projecting to find an error term, then minimizing it. They have published a number of papers going back to the late 1990’s on their approaches to the problem of tying together successive helical scans and achieving dose efficiency in doing so. They have studied artifact reduction from modeling the X-ray spectrum, noise vs. resolution in volumetric CT, tilted gantry effects, how variations in gain of adjacent detector elements cause errors when slices are combined, how to iteratively deal with beam hardening, and the effect of pitch in raw data interpolation in helical CT. All in all an impressive group.

Opportunities for TomoSoft GE is highly interested in dose reduction, and already very active in reconstruction based approaches, primarily through ART type algorithms but not exclusively. EST would only be attractive to them if it’s superiority was demonstrable and significant. However if this were the case, their interest could be piqued based on their investments so far. If it was equivalent in dose reduction but much better in speed it might also be of interest, although presumably at a lower price.

Hitachi

Intelli IP (Iterative Processing) Attempts to reduce noise from data obtained by low-dose scanning, by means of iterative processing. Uses a high speed processor. Dose reduction web pages are still “to be posted”. Clearly they are behind in this area. No references to Intelli IP found in PubMed or on the web in general. Offer Advanced Visualization Solutions from TeraRecon. This suggests their internal development group is not strong, and/or that they are open to products developed outside.

CORE method (COne-beam REconstruction) Provides option to ignore data away from the central slices (leading to wasted dose?) to improve cone-beam images.

Models offered Scenaria - 64 slice. ECLOS16 - Cost effective 16 slice non-cardiac. Dose reduction via high pitch acquisitions (moving very fast, possibly under-sampling areas).

Summary of products Hitachi is behind the leaders in both software and hardware. Their dose reduction products look more like vapor-ware than anything real. CORE seems like a desperation move to shore up a weak cone beam reconstruction algorithm by discarding the high angle data away from the central slices.


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Publication summary (see list of publications in Appendix C) Hitachi has published almost nothing in CT. As with their hardware and software offerings, they are behind the others to a great degree, at least in the US.

Opportunities for TomoSoft EST could help Hitachi catch up in the area of reconstruction and dose reduction. It is not clear they have much interest in doing anything at this point. They already use an outsourced product for visualization so might be more open than most to partnering with TomoSoft. They do have some pointers on their web site to a dose reduction effort, but little content so far. So perhaps they are interested in that topic. But they are well behind in many other areas, at least judging by the publically available information. The question is whether they are serious about staying in the CT business.

Philips

iDose and RapidView IR consoles Up to 80% dose reduction (DR), 20 images/sec reconstruction rate with RapidView IR console.

ClearRay reconstruction Method of reducing effect of scatter in Z direction, and for handling beam hardening. No references to iDose or ClearRay found in PubMed.

Models offered Brilliance iCT, iCT SP - Top of the line, 256 slices. iDose iterative reconstruction. Up to 80% less dose (cumulative from all techniques?). Brilliance CT 64-channel - ClearRay cone beam reconstruction, 4 cm coverage per rotation. Brilliance 16-slice - Basic 16 slice machine, with options of 0.4 sec rotation, 20 images per second reconstruction. Ingenuity CT - iDose4 up to 80% dose reduction, or 68% with 35% improved spatial resolution, or same dose and 68% better spatial resolution. MX 16-slice CT - compact, cost effective 16 slice system. MX 4000 Dual - entry level multi-slice imaging, 20 mm coverage. MX 6000 Dual - Simple, reliable operation, 20 mm coverage, with options for 0.8 sec rotation, 0.9 sec reconstruction, 0.8 mm slices. Spectral CT

Summary of products Philips is one of the leaders in CT hardware and software, along with GE. iDose is evidently now shipping but has not yet reached the literature, although they do show


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testimonials from early users on the website. The packaging of iDose with the RapidView workstation fits the model of a computer intensive algorithm. Their approach of offering the workstation as a retrofit to older scanners is along the same lines as TomoSoft’s thinking.

Publication summary (see list of publications in Appendix C) Philips research was more active in the early 2000’s, with only a few articles in the last few years. They were not nearly as active as GE or Siemens at the 2008 Utah conference for example. Their work has been heavily weighted toward cone beam reconstruction and removal of artifacts, in particular for cardiac imaging. There is a lot of work in the area of 1-Pi, 2-Pi, 3-Pi reconstructions which has to do with stitching together data from successive helical traverses or adjacent circle scans. Much of that work was in the early 2000’s though. They’ve also dabbled in some esoteric areas, such as photon counting approaches, off-center imaging, and phase contrast imaging. Perhaps the most important paper from TomoSoft’s perspective is from 2007, introducing OSC, the likely candidate for the technology behind iDose. OSC is discussed in Appendix A.

Opportunities for TomoSoft Philips has a large investment and a lead on most of the competition in general. The published research does not reveal anything about their work on iDose, nor even give much hint about their interest in dose reduction. Going only by the published research, one might expect them to be a candidate for licensing EST, but they do have the iDose product. However this also suggests that if EST can be demonstrated superior to their approach they should be interested. While they have an active interest in dose reduction and in staying in the lead technologically, it is questionable whether EST offers them an advantage over what they currently are working on.

Siemens

IRIS (Iterative Reconstruction in Image Space) Iterative reconstruction using a “master volume reconstruction” to speed up the processing compared to normal iterative reconstruction. Uses physical properties of the acquisition system. Typically needs 3-5 steps to converge. Siemens low dose brochure shows image with 40% less noise than standard FBP processing. Up to 60% DR claimed in Siemens IRIS brochure. One reference to IRIS found in PubMed. UFC - Ultra Fast Ceramic detectors (short afterglow).

Models offered SOMATOM Definition, Definition Flash - Dual source, 32 slices, 0.33 sec rotation. Cardiac gating and patient density specific mAs settings (CARE Dose4D) to reduce dose.


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SOMATOM Sensation, Sensation Open - 24, 40 or 64 slices. SOMATOM Emotion - Workhorse model, 6 or 16 slices. SOMATOM Spirit - Low end model, 2 slices, 0.8 sec rotation, 5 images/sec.

Summary of products Siemens is aggressive but behind in hardware, with a maximum offering of 64 detector slices (they claim twice this but by using a moving X-ray focal spot to simulate it). IRIS is new enough to not be in the literature, but appears to be real and to have had much thought put into it.

Publication summary (see list of publications in Appendix C) Siemens has an active research agenda, going back at least a decade. They were a sponsor, with GE and Toshiba, of the 2008 Utah Imaging Conference. The research covers a number of distinct areas, with forays into esoteric investigations. The prime areas involve cardiac cone beam CT, with an emphasis on fast response and artifact reduction. They study ECG gating, the use of dual sources, in novel arrangements and with short (partial) scans to further speed things up. Pitch in helical scanning is also an area of research, especially with dual source use. They also study ways to improve C-arm cone beam cardiac CT for interventional radiology applications. In the mid-2000’s they published on z-flying focal spot scanning, which is the basis for their claimed doubling of slices in recent scanners. The more esoteric areas include work in Hilbert transforms with DBP – Differentiated Back Projection. Siemens Oncology has published work in the areas of kV and MV cone beam imaging. There is only one paper found associated with their IRIS dose reduction technique.

Opportunities for TomoSoft Siemens is heavily invested in cone beam technology, despite being behind in developing high slice count scanners. In fact they have to use their z-flying spot X-ray tube to make it sound like they have twice as many slices as the actual detector array. This makes it likely they would have an interest in cone beam EST if it proves worthy. One question that arises is how will EST handle helical scanning, as opposed to circular scanning?

Toshiba (Aquilion product line)

QDS - Quantum Denoising System Filtering techniques using edge extraction, smoothing and enhancement. Filters more where there are no edges, enhances edges were it finds them.

BOOST3D Targets photon starved areas in the raw data with 3d filters to reduce streaking artifacts.

AIDR - Adaptive Iterative Dose Reduction


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Adapts number of iterations required Anticipate dose reduction up to 75% (50% less noise implies 75% less dose requirement), FDA clearance pending. No references to AIDR found in PubMed

coneXact reconstruction Used for their wider images

Models offered Aquilion One - 320 rows of 0.5 mm width. Dynamic Volume Imaging, 16 cm in 0.35 sec rotation. AIDR when cleared. Aquilion Premium - 160 rows, upgradable to 320. Low contrast resolution 2mm @ 0.3%. 8 cm coverage per rotation. AIDR when cleared. Aquilion CX - 64 rows, coneXact reconstruction, 28 images/sec reconstruction

Summary of products Toshiba’s hardware is at the leading edge in terms of the number of slices. This may or may not be significant, as beyond a certain longitudinal extent (e.g. enough to cover the entire heart) it is questionable whether the benefits outweigh the disadvantages of highly angled X-ray trajectories. Their discussions of dose reduction focus on hardware innovations such as volume reconstruction to minimize overlap and overscan, active collimation, dynamic adaptation of X-ray output, cardiac gating, and protocols to optimize dose for each patient. They take dose reduction seriously. They have several software approaches to reducing dose. The reconstruction technique, AIDR, is iterative, and is not yet FDA cleared. Claims put it in the same league as EST.

Publication summary (see list of publications in Appendix C) Toshiba did some early work on helical multi-slice scanning, looking at up to 256 slices as early as 2003. Investigated ways to reduce the integral dose in scans where the ROI is small by lowering the dose outside the ROI and modifying the reconstruction. Algorithm related work on dose reduction through their QDS (Quantum Denoising System), an adaptive filtering approach that preserves sharp edges while smoothing areas will little variation, allowing reductions up to 38%. Also some work on weighting schemes to improve image quality to allow reduced dose in cone-beam CT, based on the Feldkamp algorithm. They have also published in the popular area of 4D imaging, developing motion compensation approaches that seek to minimize the chaos of imaged edges.

Opportunities for TomoSoft Although they have not published widely on dose reduction, Toshiba takes it seriously based on their work on QDS and AIDR, so they might be interested in EST. However


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they have several software dose reduction techniques of their own. QDS and BOOST3D might operate in concert with EST as noise reduction and feature enhancements, while AIDR is a directly competing alternative reconstruction algorithm. As always the key will be how well EST performs.


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Other companies in the CT reconstruction market

Medic Vision

SafeCT Post-image reprocessing that reduces noise. Claim 50 - 80% dose reduction capability. Stand-alone GPU based calculation engine that downloads images from the PACS system, processes them, and uploads them back. FDA approved Jan 2011.

TeraRecon

Acquarius iNtuition A suite of applications for image post-processing and visualization running on their own servers. The system offers advanced analysis features with automated workflows, as well as integrated image management and remote viewing capability. It allows thin clients, including iPads and iPhones to participate in the image viewing process. Highly scalable to cover the entire hospital’s user base, this is an ambitious program. See iPad implementation at: http://www.youtube.com/watch?v=OPrjlU54Vok&feature=related

iGentle - General Enhancement and Noise Treatment with Lower Exposure. Provides filtering and sharpening tools to allow lower dose images to be acquired. Perhaps similar to SafeCT.

Summary TeraRecon has developed an extensive and impressive system for integrating the process of image management and viewing, with powerful visualization tools added to the mix. Given their interest in dose reduction based on the existence of iGentle, they may be interested in speaking with TomoSoft. The barrier here will be in getting access to the raw data from the CT scanner, as they are currently only working with data from PACS or equivalent systems.

PreXion

PreXion is a 2007 spinoff from TeraRecon, owned by the parent company XTrillion, Inc. XTrillion is also the name of their line of embedded processors designed to make fast work of creating volume images from CT and ultrasound data. PreXion provides cone beam CT scanners to dental (PreXion 3D CBCT) and industrial (micro-CT) markets. Both PreXion and XTrillion are based in Japan, although PreXion has an address in San Mateo, CA.

ReconPro PC board with XTrillion 3.0 processor Dedicated processor for doing reconstructions. Has two XTrillion processors per board with 1 GB shared memory, running 32 bit floating point operations. Scalable in the sense


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that multiple boards can be used in parallel. Both the 1 GB memory and 32 bit operation are behind the times. Note that Mac OS X and Windows 7 are both 64 bit capable, which is required to address over 4 GB of memory.

i-View 3D imaging Workstation A stand-alone workstation for industrial CT imaging. Provides fast rendering times using the VolumePro 1000 Volume Rendering Board, and viewing, editing and workflow tools. The VolumePro 1000 is 64 bit, with up to 4 GB memory. There is also a PX-230AX Image Reconstruction Board, which is 64 bit, although with just 1 GB memory.

Summary Their strength from TomoSoft’s perspective lies in the ReconPro and PX-230AX PC boards, which could be nice platforms for the EST calculations. How fast they would run and how much work it would be to port the code to them are unknown. Since they advertise that both boards use the Feldkamp algorithm, they do not appear to have any advanced reconstruction algorithms of their own, so could be a target for licensing or joint venture.

Sirona

Sirona is a German company with a wide array of dental related products, as opposed to being a specialist in CT. They trace their history back to 1877, and claim to be the first company to manufacture X-ray tubes and apparatus, in 1895. Apparently they spoke with Roentgen three days after he discovered X-rays. In 1997 Sirona Dental Systems GmbH was established, purchasing Siemens’ dental systems division.

Galileos Dental cone beam CT scanners with resolution down to 0.15 mm voxel size. The scan times are 14 sec but reconstruction times are 4.5 minutes. The scan is pulsed, with 200 views for the full rotation. Dose rates are low compared to body CT’s (68 µSv) although it is not clear what is considered low vs. high in dental radiology.

Summary It may be that Sirona would be more interested in image quality than dose reduction, particularly away from the central plane of the image, where cone beam artifacts are a known limitation.


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Appendix A – Competitive Reconstruction and Related Technologies

ART = Algebraic Reconstruction Technique

ART is a fast iterative reconstruction technique with math underpinnings of POCS (projection onto convex sets). It has trouble with noise, since noise introduces ambiguity into the solution space.

Variants include parallel ART, which is POCS compliant, and Simultaneous ART (SART) which is not. Both are attempts to further speed up the processing.

“One of a variety of iterative computed tomography CT reconstruction algorithms in which computed projections or ray sums of an estimated image are compared with the original projection measurements and the resulting errors are applied to correct the image estimate. In ART, the corrections are computed and applied on a ray-by-ray or view by view basis. The manner in which the image converges depends on the order in which the ray-sums are considered. In most ART applications, the reconstructed image is assumed to consist of an array of square pixels which are of uniform density. The computed projections are obtained by summing the values of the pixels whose centers lie within a path of finite width. The average error is then computed and added to the pixels included in the ray sum.”

From Pan et al, Utah conference (2008): 3D iterative CT reconstruction is an active research area in medical imaging. Compared with analytic reconstruction methods such as FDK, iterative methods may provide better reconstruction results for incomplete and noisy projection data. The simultaneous algebraic reconstruction technique (SART), one of the most popular iterative reconstruction methods, is applied in the cone-beam geometry for high- resolution reconstruction, with the help of graphics hardware (GPU) and total variation (TV) regularization. GPU greatly improves the efficiency of SART, which is computationally intense for CPU, and thus makes it suitable for clinical applications. TV regularization reduces the effects of noise and helps the convergence of SART for noisy data. Iterative CT reconstruction methods such as SART have been proposed since the late eighties. These methods have advantages over analytical reconstruction methods such as FDK for incomplete and noisy projection data. However, most industrial manufacturers have utilized FDK in their products so far because the high computational cost of SART hinders its practical application. Iterative reconstruction methods have recently become active again due to the rapid developments of commodity hardware, such as GPU and Cell BE processor. This hardware may greatly enhance the efficiency for SART and make SART appropriate for clinical applications. On the other hand, regularizations are usually necessary for SART to reduce the effects of noise and enhance convergence, especially for projection data with strong noise. Total-variation (TV) minimization is a good method for the regularization of SART.


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EM = Expectation Maximization

A method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. EM is an iterative method which alternates between performing an expectation (E) step, which computes the expectation of the log-likelihood evaluated using the current estimate for the latent variables, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.

FDK = Feldkamp reconstruction algorithm

The Feldkamp (or FDK) algorithm is the standard for cone beam reconstruction. It is essentially filtered back projection generalized from fan beam to three dimensions. As such it has limitations. Chief among these is distortion outside the central plane, making spatial locations unreliable. The term “cone beam artifacts” also crops up regularly, referring to the existence of artifacts in addition to the distortion. Both these issues arise from the fact that the beam is at steeper and steeper angles as the cone widens, and complementary rays (those from the opposite side of the patient) traverse each point in space from different angles.

FBP = Filtered Back Projection

This is the standard method of reconstruction in fan beam CT. Its primary benefit is that it is very fast to compute and provides good if not optimal images. The basic principle involves “spreading” the result at each rotation angle back across the entire ROI between source and detector and summing up contributions from each angle. Because this inevitably results in smearing, a so-called “ramp” filter is applied prior to the spreading. Simple FBP fails as the data becomes noisier, and requires more sophisticated filtering to improve the situation.

MAP = Maximum A Posteriori

A Bayesian approach to improving the behavior of maximum likelihood (ML) noise reduction in FBP. ML brings in a model of photon statistics but can lead to greater noise. MAP increases smoothness and avoids the “fitting to noise”. It also allows for the inclusion of prior information.

OSC = Ordered Subsets Convex

An iterative maximum likelihood based method for reconstruction. This is the technique apparently used in Philips’ iDose product. A 2007 paper introducing it compared it to FBP, suggesting it is a 2D (fan beam) algorithm. On a phantom with water and steel beads they show a 3x improvement in SNR leading to a claimed 9x improvement in dose, at the center of the image. Away from the center the difference between FBP and OSC decreases with increasing tube current; at clinical currents the improvement dose is claimed to be 4.4x. However low tube current is what is used for low dose, so perhaps the advantage still obtains away from center. How OSC performs on real images is not addressed in the 2007 paper.

OSEM = Ordered Subset Expectation Maximization


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An iterative reconstruction method.

“The OSEM package is extremely fast and provides the following benefits: mapped (non-uniform) attenuation correction, depth dependent point spread and produces less noisy, streak free images compared to FBP.”

OSEM is just a much faster version of EM which is sometimes referred to as MLEM. Also similar to MART (Multiplicative ART).

POCS = Projection Onto Convex Sets

A mathematical underpinning of certain reconstruction algorithms that evidently provides stability in the iteration process. It is useful when there are gaps in acquired data and regularly spaced data is needed.

“The key for applying the POCS method for image recovery problems is to express every piece of available knowledge about the unknown image, which is to be recovered, by a closed convex constraint set in the image space. Then, an image vector that lies in the intersection of all the constraint sets will satisfy all the available knowledge and henceforth can be taken as a solution to the recovery problem. Clearly, such a solution can be found by invoking the POCS iterative algorithm in (2). Thus, the definition of a POCS-based image recovery algorithm requires two steps in general: 1) the definition of the closed convex constraint sets that are used; and 2) the derivation of the projections onto these constraint sets. The main advantage of this approach is that it provides flexibility in incorporating prior knowledge about the unknown image into the recovery algorithm. Indeed, any type of prior knowledge can be included in a POCS-based recovery algorithm as long as it can be represented by a closed convex constraint set in the image space.”

SART = Simultaneous Algebraic Reconstruction Technique

Similar to ART but works with blocks of data rather than rows: instead of the traditional row-action formulation of ART it uses a block-iterative method: Grid correction is performed only after an entire projection image is computed. This approach, termed SART, was shown to reduce the striping artifacts in the reconstruction images obtained by row-action ART methods. The purpose of SART is to speed up ART.

SIRT = Simultaneous Iterative Reconstruction Technique

An iterative reconstruction algorithm where predicted projection measurements are computed from an estimated image and then compared with the original projection measurements. Corrections from all projection errors are then made simultaneously to arrive at a new estimated image. The process continues iteratively until satisfactory convergence is achieved.

Statistical Iterative Reconstruction


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A general term for iterative reconstruction approaches, most often associated with GE’s ASIR.

TV = Total Variation (mathematical regularization technique)

A mathematical regularization, or denoising, technique introduced by Stan Osher of UCLA in 1992. It reduces the total variation in an image (essentially the integral of the point to point differences) while requiring fidelity to the original image. Works best with piecewise smooth images, so is especially suitable to many phantoms. The regularization parameter lambda controls how much denoising is accomplished (!=0 provides no denoising), so is a parameter that must be adjusted by the user for each case.

Might be similar (in effect if not in mathematical formalism) to the adaptive filtering techniques that preserve edges. May not be as effective on real images as on simple phantoms since real images do not necessarily have “flat” regions.

Non-local regularization

The other major technique for denoising images. Unlike TV, it does not rely only on variation of the pixels in the local neighborhood. It looks at the whole image to determine the presence of edges and thus the smoothed value of a given pixel. It uses boundary information to determine regions and looks at the distance of a pixel to the nearest identified boundary in determining how it will be treated (the penalty for roughness/difference from neighbors will be less near a boundary). It allows for including prior known information, such as the edges of boundary regions.

Convexity

A convex equation simply means that it looks convex on a graph, as a parabola does for instance. Presumably this leads to easier analytical and/or computational optimization than the alternatives by allowing faster convergence of iterations.


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Appendix B – Glossary of Acronyms

AM – Alternating Minimization (reconstruction algorithm)

ART - Algebraic Reconstruction Technique

AWCR - Aperture Weighted Cardiac Reconstruction

BPF – Back Projection Filtered (like FBP but order of filtering and back projection is reversed)

CNR – Contrast to Noise Ratio

CSCT - Coherent Scatter Computed Tomography

DBP - Differentiated Back Projection (associated with Hilbert transforms)

ECR - Extended Cardiac Reconstruction

EPID - Electronic Portal Imaging Device

FBP - Filtered Back Projection (standard technique for reconstruction of fan beam images)

FOV – Field Of View

HT – Hilbert Transform

MDCT - Multi Detector CT

MSCT - Multi Slice CT

MBIR - Model based Iterative Reconstruction

ML – Maximum Likelihood

MTF – Modulation Transfer Function (measure of the resolution in line pairs per inch or mm)

OSC – Ordered Subsets Convex (maximum likelihood method)

QDS – Quantum Denoising System (edge preserving adaptive filter)

POCS - Projection Onto Convex Sets (used in ART)

SART - Simultaneous ART

SNR – Signal to Noise Ratio


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TES – Theoretically Exact and Stable (aspect of a reconstruction algorithm)


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Appendix C – Publications by the major CT vendors

GE Medical Publications

Yu, Thibault, Bouman, Sauer, Hsieh (2011) - Model based iterative reconstruction (MBIR) in helical CT, sped up 3x using non-homogeneous iterative coordinate descent (NH-ICD)

Chen, Hsieh (2009) - Prior Image Constrained Compressed Sensing (PICCS) technique to reduce the angular coverage required from ~240to 120 degree, to enable faster cardiac scans. They use a prior image to reduce the artifacts that come from having only 120 degree coverage.

Yin, De Man, Pack (2009) - Cone beam artifacts (beyond 40mm axial coverage) can be reduced with a window based analytic cone beam reconstruction that uses data from neighboring axial scans, using weighting by cone angle.

Anderton, Brown, et al (2008) - Interventional C-arm fluoroscopy and CT using GPU acceleration to simulate various aspects of the system

Cheryauka, Ferguson (2008) - SART with Total Variation regularization, deployed on GPUs

Hsieh (2008) - Dual energy imaging to reduce beam hardening and metal artifacts

Kalya, Cherone, Cheryauka (2008) - Techniques to repeatably position a C-arm cone beam imager in 3D space to minimize blurring and artifacts

Lee, Chhem (2008) - Review of hardware and software dose reduction methods relevant to whole body helical CT scanning, from 10 mSv down by 2-3x.

Riddell (2008) - Parallel ART

Riddell (2008) - Least squares algorithms, SART with low memory, high CPU requirements for GPU deployment

Riddell (2008) - Two arc trajectory for C-arm cone beam CT to reduce CB artifacts

Yu, De Man, Wang (2008) - Pursuing an optimal design for cardiac CT machines, Inverse geometry CT prototype with custom 32 spot X-ray source and rotating gantry;

Yin, De Man, Pack (2008) - Cone beam reconstructions for multiple axial exposures, with half and full scans (rotations)

Tang, Nilsen, Smolin, Lifland, Samsonov, Taha (2008) - Study of 3D weighting by pixels and by rays in reconstruction of cone beam images. Show that both improve images but expect pixel driven weighting will be used more as computational power increases.


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Tahn, Hsieh, Dong, Fan, Toth (2008) - Dose efficiency in helical cone beam CT improved with a hybrid ray-wise cone beam filtered back projection algorithm (CB-FBP). It lets them create images beyond the borders of the usual sufficiently sampled region, thus improving dose efficiency.

Thibault, Sauer, Bouman, Hsieh (2007) - Statistical iterative reconstruction (IR) technique for reduction of artifacts and improved images in 3D multi-slice helical CT. Key parts are accurate physical noise modeling and a geometric system description. Computational load remains a challenge.

Toth, Ge, Daly (2007) - Study of effect of patient centering on noise and delivered dose. Off center causes higher doses up to 140% at the surface and 33% mean from mis-centering of 2 - 6 cm, due to bowtie filtering combined with the automatic tube current controls.

Hsieh, Londt, Vass, Li, Tang, Okerlund (2006) - Step and shoot approach to cardiac/coronary artery imaging with cone beam geometry to handle irregular heart beats and lower dose compared to helical acquisition.

Hsieh, Tang (2006) - Cone beam algorithm to reduce artifacts when the gantry is tilted, using re-binning to parallel beams prior to back-projection.

Tang, Hsieh, Nilsen, Dutta, Samsonov, Hagiwara (2006) - Helical cone beam FBP algorithm, with weighting to have rays with smaller cone angles carry more weight in the reconstruction.

Tang, Hsieh, Hagiwara, Nilsen, Thibault, Drapkin (2005) - Circular cone beam FBP with weighting to reduce artifacts, focused on the conjugate rays (those 180 degrees opposite each other).

Tang, Hsieh (2004) - Rotational filtering of cone beam FBP, using all projection data to improve dose efficiency and lower noise.

Hsieh, Chao, Thibault, Grekowicz, Horst, McOlash, Myers (2004) - Technique for extending the CT scan field of view.

Walter, De Man, Iatrou, Edic (2004) - Review of the possible future directions for CT imaging

De Man, Basu (2004) - Distance-driven projection and back projection in cone beam reconstruction, to reduce artifacts and improve reconstruction speed.

More GE papers: 2009 - Development and performance evaluation of an experimental fine pitch detector multislice CT scanner. Imai Y, Nukui M, Ishihara Y, Fujishige T, Ogata K, Moritake M, Kurochi H, Ogata T, Yahata M, Tang X. 2007 - Resolution and noise trade-off analysis for volumetric CT.


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Li B, Avinash GB, Hsieh J. 2004 - Fractional scan algorithms for low-dose perfusion CT. Hsieh J, Wei Y, Wang G. 2004 - 4D-CT imaging of a volume influenced by respiratory motion on multi-slice CT. Pan T, Lee TY, Rietzel E, Chen GT. 2003 - Analytical models for multi-slice helical CT performance parameters. Hsieh J. 2002 - Dose reduction on GE CT scanners. Fox SH, Toth T. 2002 - Consistency conditions upon 3D CT data and the wave equation. Patch SK. 2002 - Dose reduction opportunities for CT scanners. Toth TL. 2000 - Four multidetector-row helical CT: image quality and volume coverage speed. Hu H, He HD, Foley WD, Fox SH. 2000 - Tomographic reconstruction for tilted helical multislice CT. Hsieh J. 2000 - A z gain nonuniformity correction for multislice volumetric CT scanners. Besson G, Hu H, Xie M, He D, Seidenschnur G, Bromberg N. 2000 - An iterative approach to the beam hardening correction in cone beam CT Hsieh J, Molthen RC, Dawson CA, Johnson RH. 1998 - Helical CT reconstruction with longitudinal filtration. Hu H, Shen Y. 1998 - New classes of helical weighting algorithms with applications to fast CT reconstruction. Besson G. 1998 - Future directions in CT technology. Fox SH, Tanenbaum LN, Ackelsberg S, He HD, Hsieh J, Hu H. 1991 - CT filtration aliasing artifacts. Crawford CR.

Hitachi Publications


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Baba, Ueda, Okabe (2004) – Developed a prototype cone beam dental CT using a flat panel detector and evaluated its properties.

Baba, Konno, Ueda, Ikeda (2002) – Evaluation of a flat panel detector vs. an image intensifier for use in cone beam CT. Preliminary work back in 2002.

Philips Publications

Kohler, Engel, Roessl (2008) - Simulated phase contrast tomographic imaging.

Schaefer, Grass (2008) - Simulated BPF versus FBP for off-center (detector closer than source) detector arrangements in circular fan beam CT.

Kohler, Brendel, Proksa (2008) - Use of a beam shaper to increase SNR by 12% by removal of redundant data in helical scans. Beam shaping is in the cone angle direction, in addition to the normal bow tie filter. The 12% improvement in SNR is equivalent to a 25% dose reduction.

Herrmann, Engel, Wiegert (2010) – Simulation study of a CT photon counting detector using a silicon top layer followed by CdZnTe. The silicon layer is there to help get the count rate down to manageable levels by sharing some of the load. Compton scatter in the Si is a problem but can be managed.

Bontus, Koken, Kohler, Grass (2006) – Cardiac gating in large angle cone beam systems. Reconstruction approach, CEnPiT, that breaks up the data into radon planes (with redundancies) and the rest (which contain low frequency axial components), where the Radon planes are reconstructed with gating and the rest are not.

Koken, Grass(2006) – Aperture weighted cardiac reconstruction (AWCR), an FBP approach for 40 and 64 slice cone beam. Uses most of the redundant data to improve image homogeneity, SNR and time resolution.

Bontus, Kohler, Proksa (2005) - Filtered back projection (FBP) using n-Pi acquisition, building on earlier work on defining filter lines along the detector. The technique gives all Radon planes the correct weighting.

Nielsen, Manzke, Proksa, Grass (2005) – Helical cardiac cone beam using ART approaches. Tested with 16 slices and simulated up to 256 slices, they find that iterative ART gives fewer cone beam artifacts at high angles than does analytical reconstruction.

Manzke, Koken, Hawkes, Grass (2005) – Helical cone beam reconstructions in cardiac imaging. Simulation study with wide angle detectors, using the Extended Cardiac Reconstruction (ECR) framework and retrospective cardiac gating.

Manzke, Grass, Hawkes (2004) – Analysis of cone beam cardiac artifacts caused by poor gating (motion artifacts).


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Grass, Manzke, Nielsen, Koken, Proksa, Natanzon, Schecter (2003) – The Extended Cardiac Reconstruction (ECR) method for combining cone beam reconstruction with retrospective cardiac gating. Up to 16 slices.

Bontus, Kohler, Proksa (2003) - 3-Pi (1 " turns) helical CT reconstruction, deriving details of the back projection algorithm having to do with where radon planes and filter lines lie, showing “quasiexactness” of their approach.

Van Stevendaal, Schlomka, Harding, Grass (2003) - Coherent Scatter Computed Tomography (CSCT) using FBP rather than the usual ART. Similar image quality but 100x faster.

Kohler, Proksa, Bontus, Grass, Timmer (2002) – Study of aliasing artifacts in four different cone beam reconstruction techniques, Advanced Single Slice Rebinning (ASSR) a 2D approximation, Pi, Pi-Slant, and 3-Pi which are all true 3D. 3-Pi’s use of redundant data helps remove artifacts, and Pi-Slant also performs well.

Kohler, Proksa, Grass (2001) – Acquisition technique and a fast reconstruction method for sequential cone beam (not helical) which provides good image quality for small cone angles while not being an exact reconstruction due to lack of complete data.

Grass, Kohler, Proksa (2001) – Determined that a distance-weighted scheme for axially truncated cone beams in circular trajectory scanning provides the best performance of four weighting schemes.

Grass, Kohler, Proksa (2000) – 3D cone beam reconstruction technique (and alternative to Feldkamp) which uses cone to parallel beam rebinning followed by rebinning to rectangular virtual detector followed by FBP. Lower complexity than Feldkamp, and allows a wider axial volume to be reconstructed by applying different reconstruction conditions for each voxel.

Siemens Publications

Petersilka, Stierstorfer, Bruder, Flohr (2010) – Dual Source CT corrections for cross scattered X-rays. Two techniques studied, one assuming the scatter is primarily from the surface, using look up tables associated with particular surface characteristics. The second uses dedicated sensors outside the beam penumbra to measure scattered radiation.

Zhan, Dewan, Zhou (2009) – A new approach to automatic image segmentation in which they develop a model which attempts to be independent of imaging modality rather than being custom tuned for it. Early stage work.

Tao, Lu, Dewan, Chen, Corso, Xuan, Salganicoff, Krishnan (2009) – Novel learning based model for detection and segmentation of Ground Glass Nodule (GGN) in lung images.

Flohr, Leng, Yu, Aijmendinger, Bruder, Petersilka, Eusemann, Stierstorfer, Schmidt, McCollough (2009) – Study of effect of pitch and slice thickness comparing single source pitch=1.0 and dual source pitch=3.2 scans in 4D imaging, on spatial and low contrast


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resolution, CT number accuracy, SSPs, image uniformity and noise. The high pitch condition led to somewhat worse artifacts for structures varying strongly in the Z direction. High heart beats also led to issues. ECG-triggered high pitch scanning led to doses lower than ECG-triggered sequential step and shoot and ECG-gated spiral with X-ray pulsing by 25% and 60% respectively.

Flohr, Raupach, Bruder (2009) – Review of potential further improvements in cardiac CT in the areas of spatial and temporal resolution and volume coverage.

Manhart, Dennerlein, Kunze (2008) – Method of reconstructing horizontally offset C-arm cone beam images. Offset enables larger field of view but increases complexity of reconstruction due to need for complementary rays and rebinning, making it impossible to do on-line. Their new technique speeds things up.

Dennerlein, Noo (2008) – C-arm cone beam reconstruction study to look at artifacts from short scans. Test of the Factorization method on simulated and real phantom and clinical data vs. FDK. Fewer artifacts with short scan Factorization, even outperforming full scan FDK.

Schoendube, Stierstorfer, Noo (2008) – Study of the inverse 2D Hilbert Transform as applied to reconstruction algorithms as in Differentiated Back Projection (DBP). 2D allows greater flexibility than 1D in arranging the back-projection grid. Ringing artifacts arise that can be controlled with Hamming apodization in many cases. Purely theoretical study done with parallel beam geometry, not tied to any hardware setup. The DBP results in the Hilbert Transform of the image so an inverse HT is required. This is an exact solution.

Yu, Noo, Dennerlein, Lauritsch, Hornegger – Attempts to use a C-arm cone beam scanner in a way that emulates diagnostic helical scanning (with a slip ring CT scanner) and how it reduces artifacts in extended volume imaging. Circles and lines are one approach to getting full coverage, and the reverse helix with lines is another that provides a more practical approach. The key is to get full coverage of the R-lines, any line segment connecting two source positions together.

Schoendube, Kunze, Bruder, Stierstorfer (2008) – Using the positivity constraint to improve cardiac motion imaging using subsets of the scan data (short scans). The algorithm attempts to be more robust and easier to implement than previous ones. They require that the non-moving parts of the image remain constant, and so remove artifacts that violate this condition. The procedure is to replace all the negative values with zeros, then perform iterative FBP.

Bruder, Raupach, Sedlmair, Stierstorfer (2008) – Iterative Weighted FBP algorithm in 4D cardiac cone-beam CT to reduce noise/reduce dose. Use nonlinear regularization to filter out noise while retaining sharpness in both space and time. Works with single and dual source CT scanners.

Rohkohl, Lauritsch, Biller, Hornegger (2008) – Algorithm for 4D C-arm cardiac vasculature CT that improves time response in non-periodic situations. Gated on an ECG, but taking into


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account an estimate of cardiac motion to enable use of more data. Improvement on a previous algorithm that only worked well for very periodic data. Runs offline on an NVIDIA graphics card over 20 minutes. Not commercially available.

Flohr, Bruder, Stierstorfer, Petersilka, Schmidt, McCollough (2008) – Image reconstruction in cardiac gated dual source CT (DSCT), where one source covers the full view and the other a smaller view. Used 3D back-projection, reduced artifacts through applications of filters in particular directions, and truncation of the extrapolated data.

Toshiba Publications

Silver (2008) – Talk discussing the differences between C-arm cone beam CT for interventional use, with slow rotation speeds and diagnostic fully enclosed multi-slice CT scanners with 10x rotation speeds. The detectors are different due to the rotation speeds, and the reconstruction algorithms are different as well.

Katsevich, Silver, Zamyatin (2008) – Motion compensated imaging technique based on local tomography, using the appearance of edges in the motion-compensated image to determine whether the motion has been properly addressed.

Okumura, Ota, Kainuma, Sayre, McNitt-Gray, Katada (2008) – Developed a dose reduction scheme for multi-slice CT using an adaptive edge preserving filter called the Quantum Denoising System (QDS). Found dose reductions up to 38%.

Tsukagoshi, Ota, Fujii, Kazama, Okumura, Johkoh (2007) – Study of the effect of different reconstruction kernels on the relative spatial resolution in XY and Z dimensions for isotropic scans.

Okumura, Ota, Tsukagoshi, Katada (2006) – Study of edge-preserving adaptive filters using a newly developed digital phantom image.

Mather (2005) – Discussion of the advantages of high numbers of slices, up to 64, in imaging not only the heart but also in neuro, for the high resolution it provides and the ability to have isotropic data sets (equal resolution in all dimensions).

Westermann (2005) – Review article on the advantages that multi-slice CT brings to cardiac imaging in particular, due to the higher resolution in Z that is available.

Chityala, Hoffmann, Rudin, Bednarek (2005) – Study of cone beam head phantom images with a filter in place to lower the dose to regions outside an ROI, using a mapping function to restiore the intensity outside the ROI and various types of filtering. They found they could get as good images inside the ROI as when the full field of view was illuminated, and got good images even outside the ROI.

Taguchi, Chiang, Silver (2004) – Dose reduction in cone-beam helical CT using a weighting scheme that takes validity of the data and redundancy into account, using a generalized version of the helical Feldkamp algorithm.


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Taguchi (2003) – Cone-beam reconstruction algorithm with high temporal resolution, using a weighting function on the time axis and filtering in the detector row direction before applying Feldkamp back-projection on a 256 slice scanner.

Westermann (2002) – Radiation dose from Toshiba CT scanners. Review article discussing Toshiba’s various efforts to reduce dose, primarily hardware and protocol based.

Taguchi, Aradate (1998) – Early article on multi-slice CT with helical scanning. They propose an algorithm which is surely out of date by now.

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Competitive Analysis of CT Vendor Technologies · Competitive Analysis of CT Vendor Technologies ... So perhaps they are interested in that topic. ... They also study ways to improve