{"id":4145,"date":"2011-05-26T08:44:45","date_gmt":"2011-05-26T08:44:45","guid":{"rendered":"http:\/\/hgpu.org\/?p=4145"},"modified":"2011-05-26T08:44:45","modified_gmt":"2011-05-26T08:44:45","slug":"optimized-gpu-framework-for-pulsed-wave-doppler-ultrasound","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=4145","title":{"rendered":"Optimized GPU Framework for Pulsed Wave Doppler Ultrasound"},"content":{"rendered":"<p>Pulsed Wave (PW) spectrum Doppler ultrasound is a valuable tool for clinical diagnosis for flow velocity distribution in vessels. However, real-time processing of PW spectrum is computationally intensive, involving wall filtering, Fast Fourier Transform (FFT), column filtering and linear averaging. In this paper a very efficient implementation of a PW Doppler spectrum ultrasound using the Compute Unified Device Architecture (CUDA) platform developed by NVIDIA is presented. By exploiting the explicit parallelism exposed in the graphics hardware we obtain more than one order speed-up gain compared with that from standard CPUs. Finally, we get a rate of 7.60 microseconds with one line of 256 samples, which is about 92 times faster than the CPU implementation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Pulsed Wave (PW) spectrum Doppler ultrasound is a valuable tool for clinical diagnosis for flow velocity distribution in vessels. However, real-time processing of PW spectrum is computationally intensive, involving wall filtering, Fast Fourier Transform (FFT), column filtering and linear averaging. In this paper a very efficient implementation of a PW Doppler spectrum ultrasound using the [&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":[89,38,3,41],"tags":[14,207,1788,20,1789,208],"class_list":["post-4145","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-medicine","category-paper","category-signal-processing","tag-cuda","tag-fft","tag-medicine","tag-nvidia","tag-signal-processing","tag-ultrasound"],"views":2054,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4145","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=4145"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4145\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4145"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4145"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4145"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}