Dynamic Load Balancing on Distributed Listmode Time-of-Flight Image Reconstruction

Hu, Zhiqiang; Wang, W.; Gualtieri, E.; Parma, Michael J.; Walsh, E.S.; Sebok, David; Hsieh, Yu-Lung; Tung, Chi-hua; Griesmer, Jerome; Kolthammer, Jeffrey; Popescu, Lucreţiu M.; Werner, Matthew E.; Karp, Joel S.; Bucur, Anca; Leeuwen, Jasper van; Gagnon, Daniel

doi:10.1109/nssmic.2006.353730

Cited by 7 publications

(6 citation statements)

References 5 publications

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“…15 A significant research effort has been devoted to exploiting GPU technology for x-ray computed tomography (CT), 16,17 digital tomosynthesis, 18 radiation treatment planning, 19 dose calculation, 20 and other medical physics applications. 21 Compared with multi-CPU clusters, 22 the faster speed and lower cost of communication between GPU cores makes scaling easier for applications that involve internode communication. This is true for most of the iterative algorithms, which require the result of the previous step to be broadcasted to all the nodes before the next iteration can begin.…”

Section: Introductionmentioning

confidence: 99%

Fully 3D list‐mode time‐of‐flight PET image reconstruction on GPUs using CUDA

et al. 2011

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A list-mode ToF OSEM library was developed on the GPU-CUDA platform. Our studies show that the GPU reformulation is considerably faster than a single-threaded reference CPU method especially for ToF processing, while producing virtually identical images. This new method can be easily adapted to enable more advanced algorithms for high resolution PET reconstruction based on additional information such as depth of interaction (DoI), photon energy, and point spread functions (PSFs).

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Section: Introductionmentioning

confidence: 99%

Fully 3D list‐mode time‐of‐flight PET image reconstruction on GPUs using CUDA

et al. 2011

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“…Until now, computer clusters have been the platform of choice for reconstructing clinical TOF PET data [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Fast List-Mode Reconstruction for Time-of-Flight PET Using Graphics Hardware

Pratx

Surti

Levin

2011

IEEE Trans. Nucl. Sci.

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Positron emission tomography (PET) measurements with time-of-flight (TOF) information are often very sparse. As a result, direct reconstruction from raw list-mode data is an attractive strategy for dealing with the large dimension spanned by the measurements. However, even though sparse datasets are more efficiently processed in list mode than as sinograms, list-mode reconstruction remains computationally demanding and computer clusters are typically required for reconstructing clinical PET scans with TOF information. In this work, we demonstrate that off-theshelf graphics processing units can be used as an alternative approach to accelerate line projections with TOF kernels.

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“…The list-mode time-of-flight OSEM PET algorithm implemented on the PET reconstruction platform (22) was modified to incorporate the fat tissue constraint into the system matrix, in a similar way to that in which timeof-flight functionality is incorporated. The list-mode time-of-flight (TOF) OSEM update equation can thus be written as follows:…”

Section: Pet-constrained Reconstructionmentioning

confidence: 99%

Fat-Constrained ¹⁸F-FDG PET Reconstruction in Hybrid PET/MR Imaging

Prevrhal

Heinzer²,

Wülker

et al. 2014

J Nucl Med

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Fusion of information from PET and MR imaging can increase the diagnostic value of both modalities. This work sought to improve 18 F FDG PET image quality by using MR Dixon fat-constrained images to constrain PET image reconstruction to low-fat regions, with the working hypothesis that fatty tissue metabolism is low in glucose consumption. Methods: A novel constrained PET reconstruction algorithm was implemented via a modification of the system matrix in list-mode timeof-flight ordered-subsets expectation maximization reconstruction, similar to the way time-of-flight weighting is incorporated. To demonstrate its use in PET/MR imaging, we modeled a constraint based on fat/water-separating Dixon MR images that shift activity away from regions of fat tissue during PET image reconstruction. PET and MR imaging scans of a modified National Electrical Manufacturers Association/ International Electrotechnical Commission body phantom simulating body fat/water composition and in vivo experiments on 2 oncology patients were performed on a commercial time-of-flight PET/MR imaging system. Results: Fat-constrained PET reconstruction visibly and quantitatively increased resolution and contrast between high-uptake and fatty-tissue regions without significantly affecting the images in nonfat regions. Conclusion: The incorporation of MR tissue information, such as fat, in image reconstruction can improve the quality of PET images. The combination of a variety of potential other MR tissue characteristics with PET represents a further justification for merging MR data with PET data in hybrid systems. PET/ MR is rapidly emerging as a promising new hybrid imaging modality. Among several differentiating features when compared with PET/CT, the combination of PET with MR imaging can potentially benefit from reduced radiation for the patient and predominantly from the wider range of tissue characteristics generated by appropriate MR imaging sequences and their multiparametric combinations (1). MR provides anatomic imaging with better soft-tissue depiction and more dedicated tissue contrast than CTand furthermore offers insight into a variety of aspects of human physiology with the potential to image metabolic processes and physiologic function (2-4), complementing metabolic and molecular information afforded by PET for better assessment of the underlying physiopathology (5). Beyond these diagnostic aspects, in the hybrid setting with PET, MR data are also used to estimate tissuedependent photon attenuation and scatter to improve the quantitative accuracy of PET images (6-8). The MR-based motion correction of PET reconstruction to improve PET image quality is also supported (9,10).Spatial resolution and quantification accuracy in PET imaging are limited by the low detector spatial resolution and poor photon counting statistics. Early developments in PET reconstruction techniques have addressed this limitation by modeling the underlying photon generation statistics and taking advantage of the physics of coincidence detection to find an app...

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Dynamic Load Balancing on Distributed Listmode Time-of-Flight Image Reconstruction

Cited by 7 publications

References 5 publications

Fully 3D list‐mode time‐of‐flight PET image reconstruction on GPUs using CUDA

Fully 3D list‐mode time‐of‐flight PET image reconstruction on GPUs using CUDA

Fast List-Mode Reconstruction for Time-of-Flight PET Using Graphics Hardware

Fat-Constrained ¹⁸F-FDG PET Reconstruction in Hybrid PET/MR Imaging

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Dynamic Load Balancing on Distributed Listmode Time-of-Flight Image Reconstruction

Cited by 7 publications

References 5 publications

Fully 3D list‐mode time‐of‐flight PET image reconstruction on GPUs using CUDA

Fully 3D list‐mode time‐of‐flight PET image reconstruction on GPUs using CUDA

Fast List-Mode Reconstruction for Time-of-Flight PET Using Graphics Hardware

Fat-Constrained 18F-FDG PET Reconstruction in Hybrid PET/MR Imaging

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Fat-Constrained ¹⁸F-FDG PET Reconstruction in Hybrid PET/MR Imaging