Three-dimensional computed tomography (CT) is a compute-intensive process, due to the large amounts of source and destination data, and this limits the speed at which a reconstruction can be obtained. There are two main approaches to cope with this problem: (i) lowering the overall computational complexity via algorithmic means, and\/or (ii) running CT on specialized high-performance hardware. Since the latter requires considerable capital investment into rather inflexible hardware, the former option is all one has typically available in a traditional CPU-based computing environment. However, the emergence of programmable commodity graphics hardware (GPUs) has changed this situation in a decisive way. In this paper, we show that GPUs represent a commodity high-performance parallel architecture that resonates very well with the computational structure and operations inherent to CT. Using formal arguments as well as experiments we demonstrate that GPU-based ‘brute-force’ CT (i.e., CT at regular complexity) can be significantly faster than CPU-based as well as GPU-based CT with optimal complexity, at least for practical data sizes. Therefore, the answer to the title question: “Can GPU-based processing beat complexity optimization for CT?” is “Absolutely!”.<\/p>\n","protected":false},"excerpt":{"rendered":"
Three-dimensional computed tomography (CT) is a compute-intensive process, due to the large amounts of source and destination data, and this limits the speed at which a reconstruction can be obtained. There are two main approaches to cope with this problem: (i) lowering the overall computational complexity via algorithmic means, and\/or (ii) running CT on specialized […]<\/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":[33,38,3],"tags":[479,478,1786,512,1788,20,183],"class_list":["post-1851","post","type-post","status-publish","format-standard","hentry","category-image-processing","category-medicine","category-paper","tag-computed-tomography","tag-ct","tag-image-processing","tag-image-reconstruction","tag-medicine","tag-nvidia","tag-nvidia-geforce-8800-gtx"],"views":2024,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1851","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=1851"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1851\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1851"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1851"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1851"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}