The Siemens ECAT HRRT PET scanner has the potential to produce images of the human brain with spatial resolution better than 3 mm. MOLAR (a motion-compensation OSEM List-mode Algorithm for Resolution-recovery) was developed to provide reconstructions of HRRT data with the best possible accuracy and precision. However, a computer cluster is required to generate reconstructions in a reasonable amount of time. Strategies for computational efficiency have already been implemented in MOLAR but room for improvement remains. In this study we have begun the process of converting time- consuming components of MOLAR to parallelized code that runs on commodity graphics cards (GPUs) with much faster turnaround. We evaluated the performance of list-mode event forward projections and component-based normalization factor calculations, and we confirmed the numerical accuracy of images reconstructed with GPU-assisted code running on an HP xw8400 workstation with an NVIDIA Quadro FX 4600 graphics card. We evaluated simulated data projected through a 128times128times128 image volume that included the direct calculation of a gaussian resolution function for simulated list-mode events. This was done using the Cg and CUDA programming APIs for implementation comparison. Both GPU versions ran up to 100 times faster than the CPU-only code. The CUDA version showed some improvement over Cg and was easier to program. We also examined measured Ge-68 phantom data projected through a 256times256times207 image volume with resolution functions obtained through array lookup rather than by direct calculation. The GPU-assisted code was observed to be up to 14 times faster than the CPU-only code, particularly when one million or more events were processed. Normalization processing was found to be up to 36 times faster. However, speedup decreased to a factor of 3 when disk I\/O became dominant as more than one billion events were processed. We anticipate further acceleration of MOLAR as we convert other -components to GPU-assisted code, in particular backprojection and scatter correction. (Backprojection is on hold until a next generation GPU which has atomic write capability becomes available.)<\/p>\n","protected":false},"excerpt":{"rendered":"
The Siemens ECAT HRRT PET scanner has the potential to produce images of the human brain with spatial resolution better than 3 mm. MOLAR (a motion-compensation OSEM List-mode Algorithm for Resolution-recovery) was developed to provide reconstructions of HRRT data with the best possible accuracy and precision. However, a computer cluster is required to generate reconstructions […]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[89,33,38,3],"tags":[14,1786,512,1788,20,823,567],"class_list":["post-3441","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-image-processing","category-medicine","category-paper","tag-cuda","tag-image-processing","tag-image-reconstruction","tag-medicine","tag-nvidia","tag-nvidia-quadro-fx-4600","tag-tomography"],"views":2337,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3441","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3441"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3441\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3441"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3441"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3441"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}