Fast image reconstruction is a critical requirement for an imaging modality to be adopted in the field of clinical and pre-clinical sciences. While programs become faster due to more powerful hardware, at the same time data size increases and the need for advanced—and often computational more demanding—reconstruction algorithms arises. A cheap way to achieve a major speed-up is to utilize modern graphics hardware capable of executing algorithms in a massively parallel manner. In this article, the open source library AGILE designed for image reconstruction in biomedical sciences is introduced. Its modular, object-oriented and templated design eases the integration of the library into user code. Furthermore, applications from the field of magnetic resonance imaging and fluorescence tomography are presented. As demonstrated, a speed-up of factor 10–30 is achievable with commodity graphics hardware for different reconstruction tasks.<\/p>\n","protected":false},"excerpt":{"rendered":"
Fast image reconstruction is a critical requirement for an imaging modality to be adopted in the field of clinical and pre-clinical sciences. While programs become faster due to more powerful hardware, at the same time data size increases and the need for advanced—and often computational more demanding—reconstruction algorithms arises. A cheap way to achieve a […]<\/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":[36,89,33,38,3],"tags":[1787,14,1786,512,172,1788,807,20,1232,379,176,567],"class_list":["post-7510","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-image-processing","category-medicine","category-paper","tag-algorithms","tag-cuda","tag-image-processing","tag-image-reconstruction","tag-magnetic-resonance-imaging","tag-medicine","tag-mri","tag-nvidia","tag-nvidia-geforce-gts-450","tag-nvidia-geforce-gtx-480","tag-package","tag-tomography"],"views":2507,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7510","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=7510"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7510\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7510"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7510"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7510"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}