Cardiac electrophysiological simulation and multi-modality visualization are computationally intensive and valuable in studying the structure, mechanism, and dynamics of heart. The existing multi-CPU based approaches can reduce the calculation time, but suffer from the hardware and communication cost problems and are inefficient for 3D data visualization. Compared with multi-CPU, the highly parallel and multi-core properties of GPU make it a suitable alternative for accelerating cardiac simulation and visualization. In this paper, we develop a G-Heart system where GPU-based acceleration technologies are adopted for both the simulation of cardiac electrophysiological activities and the online illustration 3D multi-modality (anatomical and electrophysiological) data. In the simulation stage, a phase-field method is employed to cope with the no-flux boundary condition. For heart geometrical structure illustration, a GPU-based ray-casting volume rendering algorithm is implemented and an improved context-preserving model with user interaction is integrated into the proposed framework. Finally, a fusion visualization method is proposed, which can provide 3D visualization results for both the simulation data and the anatomical data simultaneously.<\/p>\n","protected":false},"excerpt":{"rendered":"
Cardiac electrophysiological simulation and multi-modality visualization are computationally intensive and valuable in studying the structure, mechanism, and dynamics of heart. The existing multi-CPU based approaches can reduce the calculation time, but suffer from the hardware and communication cost problems and are inefficient for 3D data visualization. Compared with multi-CPU, the highly parallel and multi-core properties […]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[89,38,3],"tags":[14,858,1788,20,144,199,134],"class_list":["post-11478","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-medicine","category-paper","tag-cuda","tag-heart","tag-medicine","tag-nvidia","tag-rendering","tag-tesla-c1060","tag-visualization"],"views":2690,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11478","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=11478"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11478\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11478"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11478"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11478"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}