To foster innovative uses of micro-ultrasound in biomedicine, it is beneficial to develop flexible research-purpose systems that allow researchers to easily reconfigure its system-level operations such as transmit firing sequence and receive processing. In this paper, we present the development of a programmable micro-ultrasound research platform that is capable of realizing various micro-imaging algorithms. The research platform comprises a linear-array-based scanning front-end and a PC-based data processing back-end, which employs a graphical processing unit (GPU) as the processor core. The front-end operations can be configured from the PC via the parallel port and the two blocks are synchronized by an external clock. Acquired data from the front-end is first digitized and relayed to the PC through an data acquisition card (200 MHz, 14-bit). They are then transferred to the GPU (GTX 275) in which the image formation is carried out via multi-thread processing. Results are displayed on-screen in real-time and can be saved to the PC’s hard disk for offline analysis. Through a module-based programming approach, this platform can facilitate realization of custom-designed imaging algorithms developed by researchers. In this work, B-mode imaging and adaptive color flow imaging have been implemented as demonstrations of the research platform’s programmability. The performance results show that real-time processing frame rates can be achieved for both imaging modes.<\/p>\n","protected":false},"excerpt":{"rendered":"
To foster innovative uses of micro-ultrasound in biomedicine, it is beneficial to develop flexible research-purpose systems that allow researchers to easily reconfigure its system-level operations such as transmit firing sequence and receive processing. In this paper, we present the development of a programmable micro-ultrasound research platform that is capable of realizing various micro-imaging algorithms. The […]<\/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":[14,1788,20,373,208],"class_list":["post-4763","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-medicine","category-paper","tag-cuda","tag-medicine","tag-nvidia","tag-nvidia-geforce-gtx-275","tag-ultrasound"],"views":2149,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4763","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=4763"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4763\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4763"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4763"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4763"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}