PURPOSE: The graphical processing unit (GPU) on modern graphics cards offers the possibility of accelerating arithmetically intensive tasks. By splitting the work into a large number of independent jobs, order-of-magnitude speedups are reported. In this article, the possible speedup of PLATO’s ray tracing algorithm for dose calculations using a GPU is investigated. METHODS: A GPU version of the ray tracing algorithm was implemented using NVIDIA’s CUDA, which extends the standard C language with functionality to program graphics cards. The developed algorithm was compared based on the accuracy and speed to a multithreaded version of the PLATO ray tracing algorithm. This comparison was performed for three test geometries, a phantom and two radiotherapy planning CT datasets (a pelvic and a head-and-neck case). For each geometry, four different source positions were evaluated. In addition to this, for the head-and-neck case also a vertex field was evaluated. RESULTS: The GPU algorithm was proven to be more accurate than the PLATO algorithm by elimination of the look-up table for z indices that introduces discretization errors in the reference algorithm. Speedups for ray tracing were found to be in the range of 2.1-10.1, relative to the multithreaded PLATO algorithm running four threads. For dose calculations the speedup measured was in the range of 1.5-6.2. For the speedup of both the ray tracing and the dose calculation, a strong dependency on the tested geometry was found. This dependency is related to the fraction of air within the patient’s bounding box resulting in idle threads. CONCLUSIONS: With the use of a GPU, ray tracing for dose calculations can be performed accurately in considerably less time. Ray tracing was accelerated, on average, with a factor of 6 for the evaluated cases. Dose calculation for a single beam can typically be carried out in 0.6-0.9 s for clinically realistic datasets. These findings can be used in conventional planning to enable (nearly) real-time dose calculations. Also the importance for treatment optimization techniques is evident.<\/p>\n","protected":false},"excerpt":{"rendered":"
PURPOSE: The graphical processing unit (GPU) on modern graphics cards offers the possibility of accelerating arithmetically intensive tasks. By splitting the work into a large number of independent jobs, order-of-magnitude speedups are reported. In this article, the possible speedup of PLATO’s ray tracing algorithm for dose calculations using a GPU is investigated. METHODS: A GPU […]<\/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":[479,478,14,1786,1788,20,655],"class_list":["post-3253","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-image-processing","category-medicine","category-paper","tag-computed-tomography","tag-ct","tag-cuda","tag-image-processing","tag-medicine","tag-nvidia","tag-radiotherapy"],"views":2298,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3253","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=3253"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3253\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3253"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3253"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3253"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}