{"id":6771,"date":"2011-12-30T23:54:29","date_gmt":"2011-12-30T21:54:29","guid":{"rendered":"http:\/\/hgpu.org\/?p=6771"},"modified":"2011-12-30T23:54:29","modified_gmt":"2011-12-30T21:54:29","slug":"building-human-brain-network-in-3d-coefficient-map-determined-by-x-ray-microtomography","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6771","title":{"rendered":"Building Human Brain Network in 3D Coefficient Map Determined by X-ray Microtomography"},"content":{"rendered":"<p>X-ray microtomography can visualize 3D structures of biological soft tissues at cellular to subcellular resolution. Such 3D structures are composed of a great number of cells and extracellular matrices that should be assigned separately as tissue constituents. Here, we report a method for building a skeletonized model of the human brain network in a 3D distribution map of linear absorption coefficients determined by microtomography. The 3D models of neurons were automatically built by using a Sobel filter and manually edited via a graphical interface. The simplification of the 3D coefficient map facilitates understanding of microtomographic structures composed of huge numbers of voxels. We suggest that x-ray microtomography along with model building in the 3D coefficient map is a potential method for understanding 3D microstructures relevant to biological functions, like x-ray crystallography in molecular biology.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>X-ray microtomography can visualize 3D structures of biological soft tissues at cellular to subcellular resolution. Such 3D structures are composed of a great number of cells and extracellular matrices that should be assigned separately as tissue constituents. Here, we report a method for building a skeletonized model of the human brain network in a 3D [&hellip;]<\/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":[10,89,38,3],"tags":[1781,14,512,1788,20,953,176,567],"class_list":["post-6771","post","type-post","status-publish","format-standard","hentry","category-biology","category-nvidia-cuda","category-medicine","category-paper","tag-biology","tag-cuda","tag-image-reconstruction","tag-medicine","tag-nvidia","tag-nvidia-geforce-gtx-470","tag-package","tag-tomography"],"views":2034,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6771","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=6771"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6771\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6771"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6771"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6771"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}