Quantitative sodium magnetic resonance imaging permits noninvasive measurement of the tissue sodium concentration (TSC) bioscale in the brain. Computing the TSC bioscale requires reconstructing and combining multiple datasets acquired with a non-Cartesian acquisition that highly oversamples the center of k-space. Even with an optimized implementation of the algorithm to compute TSC, the overall processing time exceeds the time required to collect data from the human subject. Such a mismatch presents a challenge for sustained sodium imaging to avoid a growing data backlog and provide timely results. The most computationally intensive portions of the TSC calculation have been identified and accelerated using a consumer graphics processing unit (GPU) in addition to a conventional central processing unit (CPU). A recently developed data organization technique called Compact Binning was used along with several existing algorithmic techniques to maximize the scalability and performance of these computationally intensive operations. The resulting GPU+CPU TSC bioscale calculation is more than 15 times faster than a CPU-only implementation when processing 256x256x256 data and 2.4 times faster when processing 128x128x128 data. This eliminates the possibility of a data backlog for quantitative sodium imaging. The accelerated quantification technique is suitable for general three-dimensional non-Cartesian acquisitions and may enable more sophisticated imaging techniques that acquire even more data to be used for quantitative sodium imaging. (c) 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 29-35, 2013.<\/p>\n","protected":false},"excerpt":{"rendered":"
Quantitative sodium magnetic resonance imaging permits noninvasive measurement of the tissue sodium concentration (TSC) bioscale in the brain. Computing the TSC bioscale requires reconstructing and combining multiple datasets acquired with a non-Cartesian acquisition that highly oversamples the center of k-space. Even with an optimized implementation of the algorithm to compute TSC, the overall processing time […]<\/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":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,10,89,3],"tags":[1787,1781,14,207,512,172,807,20,378],"class_list":["post-9473","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-biology","category-nvidia-cuda","category-paper","tag-algorithms","tag-biology","tag-cuda","tag-fft","tag-image-reconstruction","tag-magnetic-resonance-imaging","tag-mri","tag-nvidia","tag-tesla-c2050"],"views":2485,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9473","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=9473"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9473\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9473"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9473"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9473"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}