This paper describes the fast point-based 2-D\/3-D registration that will increase the registration speed of intraoperative two-dimensional (2-D) fluoroscopy and preoperative three-dimensional (3-D) CT images using GPU (graphics processing unit)-based DRR’s preprocessing. Rigid 2-D\/3-D registration of 2-D fluoroscopy images with 3-D CT images can be used for image-guided surgery or intraoperative navigation. X-ray fluoroscopy images provide real-time visualization. However, in general, resolution of fluoroscopy images is limited, these modalities are only 2-D and features in the front of body overlap one. Because of its drawback, three-dimensional imaging modalities such as computed tomography (CT) and magnetic resonance (MR) imaging are broadly used in clinical diagnostics and treatment planning [1], These have the spatial information and high resolution, but at present their use as interventional imaging modalities has been limited. In this paper, to utilize CT information during interventional procedures, a preoperative CT scan is aligned with an intraoperative X-ray fluoroscopy image. In preprocessing procedure we generate CT-derived DRRs using graphic hardware. This method is over 150 times faster than software rendering. And for registration accuracy and speed, we propose point-based 2D-3Dregistration of phantom dataset. And to reduce computation cost, we apply point-based registration technique. Because this method leads the computation time to about one second, the registration speed is enough to apply to intervention.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper describes the fast point-based 2-D\/3-D registration that will increase the registration speed of intraoperative two-dimensional (2-D) fluoroscopy and preoperative three-dimensional (3-D) CT images using GPU (graphics processing unit)-based DRR’s preprocessing. Rigid 2-D\/3-D registration of 2-D fluoroscopy images with 3-D CT images can be used for image-guided surgery or intraoperative navigation. X-ray fluoroscopy images […]<\/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":[33,38,3],"tags":[479,478,1786,172,1788,807,144,567],"class_list":["post-4044","post","type-post","status-publish","format-standard","hentry","category-image-processing","category-medicine","category-paper","tag-computed-tomography","tag-ct","tag-image-processing","tag-magnetic-resonance-imaging","tag-medicine","tag-mri","tag-rendering","tag-tomography"],"views":1937,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4044","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=4044"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4044\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4044"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4044"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4044"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}