{"id":4037,"date":"2011-05-18T10:23:35","date_gmt":"2011-05-18T10:23:35","guid":{"rendered":"http:\/\/hgpu.org\/?p=4037"},"modified":"2011-05-18T10:23:35","modified_gmt":"2011-05-18T10:23:35","slug":"optimized-gpu-framework-for-ultrasound-b-mode-imaging","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=4037","title":{"rendered":"Optimized GPU Framework for Ultrasound B-Mode Imaging"},"content":{"rendered":"<p>Ultrasound B-mode imaging is the basic image mode which can offer anatomic information of organs for clinical diagnosis. Because of the massive computation involved in baseband processing from focused radio-frequency (RF) signals followed by envelop detection, compression and scan conversion required for high quality B-mode imaging, existing medical systems always rely on complicated hardware in real time implementation. In this paper, we have proposed the parallel processing of RF signals to B-mode image based on commercial graphics processing unit (GPU) under NVIDIA CUDA platform. Experiments show that our GPU implementation can achieve a frame rate about 48 fps from RF signals of 206 x 2172 samples to an image with a size of 256 x 512. This is about 52 times faster than that based on CPU platform with the same image quality.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Ultrasound B-mode imaging is the basic image mode which can offer anatomic information of organs for clinical diagnosis. Because of the massive computation involved in baseband processing from focused radio-frequency (RF) signals followed by envelop detection, compression and scan conversion required for high quality B-mode imaging, existing medical systems always rely on complicated hardware in [&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":[89,33,38,3],"tags":[14,1786,1788,20,208],"class_list":["post-4037","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-image-processing","category-medicine","category-paper","tag-cuda","tag-image-processing","tag-medicine","tag-nvidia","tag-ultrasound"],"views":2553,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4037","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=4037"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4037\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4037"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4037"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4037"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}