{"id":3588,"date":"2011-04-14T20:45:52","date_gmt":"2011-04-14T20:45:52","guid":{"rendered":"http:\/\/hgpu.org\/?p=3588"},"modified":"2011-04-14T20:45:52","modified_gmt":"2011-04-14T20:45:52","slug":"implicit-feature-based-alignment-system-for-radiotherapy","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3588","title":{"rendered":"Implicit Feature-Based Alignment System for Radiotherapy"},"content":{"rendered":"<p>In this paper we present a robust alignment algorithm for correcting the effects of out-of-plane rotation to be used for automatic alignment of the Computed Tomography (CT) volumes and the generally low quality fluoroscopic images for radiotherapy applications. Analyzing not only in-plane but also out-of-plane rotation effects on the Dignitary Reconstructed Radiograph (DRR) images, we develop simple alignment algorithm that extracts a set of implicit features from DRR. Using these SIFT-based features, we align DRRs with the fluoroscopic images of the patient and evaluate the alignment accuracy. We compare our approach with traditional techniques based on gradient-based operators and show that our algorithm performs faster while in most cases delivering higher accuracy.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper we present a robust alignment algorithm for correcting the effects of out-of-plane rotation to be used for automatic alignment of the Computed Tomography (CT) volumes and the generally low quality fluoroscopic images for radiotherapy applications. Analyzing not only in-plane but also out-of-plane rotation effects on the Dignitary Reconstructed Radiograph (DRR) images, we [&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":[38,3],"tags":[479,478,1788,655,220,567],"class_list":["post-3588","post","type-post","status-publish","format-standard","hentry","category-medicine","category-paper","tag-computed-tomography","tag-ct","tag-medicine","tag-radiotherapy","tag-sift","tag-tomography"],"views":1679,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3588","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=3588"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3588\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3588"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3588"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3588"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}