{"id":2045,"date":"2010-12-13T21:15:01","date_gmt":"2010-12-13T21:15:01","guid":{"rendered":"http:\/\/hgpu.org\/?p=2045"},"modified":"2010-12-13T21:15:01","modified_gmt":"2010-12-13T21:15:01","slug":"development-of-a-gpu-based-multithreaded-software-application-to-calculate-digitally-reconstructed-radiographs-for-radiotherapy","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2045","title":{"rendered":"Development of a GPU-based multithreaded software application to calculate digitally reconstructed radiographs for radiotherapy"},"content":{"rendered":"<p>To provide faster calculation of digitally reconstructed radiographs (DRRs) in patient-positioning verification, we developed and evaluated a graphic processing unit (GPU)-based DRR software application and compared it with a central processing unit (CPU)-based application. The evaluation metrics were calculation speed and image quality for various slice thicknesses. The results showed that the GPU-based DRR computation was an average of 50 times faster than the CPU-based methodology, whereas the image quality was very similar. This excellent performance may increase the accuracy of patient positioning and improve the patient treatment throughput time.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>To provide faster calculation of digitally reconstructed radiographs (DRRs) in patient-positioning verification, we developed and evaluated a graphic processing unit (GPU)-based DRR software application and compared it with a central processing unit (CPU)-based application. The evaluation metrics were calculation speed and image quality for various slice thicknesses. The results showed that the GPU-based DRR computation [&hellip;]<\/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":[479,478,14,512,1788,20,357,655],"class_list":["post-2045","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-medicine","category-paper","tag-computed-tomography","tag-ct","tag-cuda","tag-image-reconstruction","tag-medicine","tag-nvidia","tag-nvidia-geforce-8800-gts","tag-radiotherapy"],"views":2086,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2045","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=2045"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2045\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2045"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2045"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2045"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}