Filtered back-projection algorithms are widely used for the reconstruction of volumetric data from cone-beam projections in interventional C-arm computed tomography. Furthermore, general-purpose GPUs have become a popular tool for accelerating the reconstruction during time-critical clinical procedures. In this work, we focus on the systematic performance optimization of cone-beam back-projection on the latest architecture of CUDA-enabled GPUs. Our optimization approach is based on the identification of the major performance bottleneck through the analysis of specifically modified kernels. Our main contribution is a smart restructuring of the backprojection algorithm that facilitates the simultaneous processing of a large number of projections and improves the hit rate of the texture cache at the same time. We use the well-known RabbitCT benchmark to demonstrate the outstanding performance of our implementation on a single Kepler-based GeForce GTX 680 GPU. Our implementation performs the back-projection of 496 input projections onto a cubic 5123 volume in less than one second, which is three times as fast as the best competing implementation. Our back-projection implementation is also able to reconstruct a cubic 10243 volume in about six seconds, which is six times as fast as the best competing implementation known to us.<\/p>\n","protected":false},"excerpt":{"rendered":"
Filtered back-projection algorithms are widely used for the reconstruction of volumetric data from cone-beam projections in interventional C-arm computed tomography. Furthermore, general-purpose GPUs have become a popular tool for accelerating the reconstruction during time-critical clinical procedures. In this work, we focus on the systematic performance optimization of cone-beam back-projection on the latest architecture of CUDA-enabled […]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","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":[479,478,14,1788,20,1306,1006,567],"class_list":["post-10151","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-medicine","category-paper","tag-computed-tomography","tag-ct","tag-cuda","tag-medicine","tag-nvidia","tag-nvidia-geforce-gtx-680","tag-tesla-c2070","tag-tomography"],"views":3068,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10151","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=10151"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10151\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10151"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10151"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10151"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}