Accurate scatter correction is especially important for high-resolution 3D PETs due to the lack of inter-slice septa. To address this problem, a fully 3D iterative scatter-corrected OSEM in which a 3D single scatter simulation (SSS) is alternatively performed with a 3D OSEM reconstruction until convergence was recently proposed. However, due to the computational complexity of both SSS and OSEM algorithms for a high-resolution 3D PET, it has not been widely used in practice. The main objective of this paper is, therefore, to accelerate the fully 3D iterative scatter-corrected OSEM using a Graphics Processing Unit (GPU) and verify its performance. For SSS implementation, a sinogram-driven approach is found to be more appropriate compared to a detector-driven approach, as fast linear interpolation can be performed in sinogram domain through the use of texture memory. Furthermore, a pixel-driven backprojector and a ray-driven projector can be significantly accelerated by assigning threads to voxels and sinograms, respectively. Using Nvidia’s GPU (Tesla C1060) and the Compute Unified Device Architecture (CUDA), the execution time of the SSS is less than 25(s), a single iteration of OSEM takes 25(s) for the span 9 HRRT geometry.<\/p>\n","protected":false},"excerpt":{"rendered":"
Accurate scatter correction is especially important for high-resolution 3D PETs due to the lack of inter-slice septa. To address this problem, a fully 3D iterative scatter-corrected OSEM in which a 3D single scatter simulation (SSS) is alternatively performed with a 3D OSEM reconstruction until convergence was recently proposed. However, due to the computational complexity of […]<\/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":[89,38,3],"tags":[14,512,1788,20,591,199],"class_list":["post-4441","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-medicine","category-paper","tag-cuda","tag-image-reconstruction","tag-medicine","tag-nvidia","tag-pet","tag-tesla-c1060"],"views":2464,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4441","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=4441"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4441\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4441"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4441"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4441"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}