We realized a real-time dual-mode standard\/complex Fourier-domain optical coherence tomography (FD-OCT) system using graphics processing unit (GPU) accelerated 4D (3D+time) signal processing and visualization. For both standard and complex FD-OCT modes, the signal processing tasks were implemented on a dual-GPUs architecture that included lambda-to-k spectral re-sampling, fast Fourier transform (FFT), modified Hilbert transform, logarithmic-scaling, and volume rendering. The maximum A-scan processing speeds achieved are >3,000,000 line\/s for the standard 1024-pixel-FD-OCT, and >500,000 line\/s for the complex 1024-pixel-FD-OCT. Multiple volumerendering of the same 3D data set were preformed and displayed with different view angles. The GPU-acceleration technique is highly cost-effective and can be easily integrated into most ultrahigh speed FD-OCT systems to overcome the 3D data processing and visualization bottlenecks.<\/p>\n","protected":false},"excerpt":{"rendered":"
We realized a real-time dual-mode standard\/complex Fourier-domain optical coherence tomography (FD-OCT) system using graphics processing unit (GPU) accelerated 4D (3D+time) signal processing and visualization. For both standard and complex FD-OCT modes, the signal processing tasks were implemented on a dual-GPUs architecture that included lambda-to-k spectral re-sampling, fast Fourier transform (FFT), modified Hilbert transform, logarithmic-scaling, and […]<\/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":[14,207,1788,20,379,144,1789,567,134],"class_list":["post-6537","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-nvidia-geforce-gtx-480","tag-rendering","tag-signal-processing","tag-tomography","tag-visualization"],"views":2073,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6537","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=6537"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6537\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6537"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6537"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6537"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}