{"id":26157,"date":"2024-02-24T11:52:14","date_gmt":"2024-02-24T12:52:14","guid":{"rendered":"https:\/\/express24.ir\/d\/product\/%d9%85%d9%82%d8%a7%d9%84%d9%87-%db%8c%da%a9-%d8%b1%d9%88%db%8c%da%a9%d8%b1%d8%af-%d9%85%d8%a8%d8%aa%d9%86%db%8c-%d8%a8%d8%b1-%da%af%d8%b1%d8%a7%d8%af%db%8c%d8%a7%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-%d8%ac\/"},"modified":"2024-02-24T11:52:15","modified_gmt":"2024-02-24T12:52:15","slug":"%d9%85%d9%82%d8%a7%d9%84%d9%87-%db%8c%da%a9-%d8%b1%d9%88%db%8c%da%a9%d8%b1%d8%af-%d9%85%d8%a8%d8%aa%d9%86%db%8c-%d8%a8%d8%b1-%da%af%d8%b1%d8%a7%d8%af%db%8c%d8%a7%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-%d8%ac","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%d9%85%d9%82%d8%a7%d9%84%d9%87-%db%8c%da%a9-%d8%b1%d9%88%db%8c%da%a9%d8%b1%d8%af-%d9%85%d8%a8%d8%aa%d9%86%db%8c-%d8%a8%d8%b1-%da%af%d8%b1%d8%a7%d8%af%db%8c%d8%a7%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-%d8%ac\/","title":{"rendered":"\u0645\u0642\u0627\u0644\u0647 \u06cc\u06a9 \u0631\u0648\u06cc\u06a9\u0631\u062f \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 \u06af\u0631\u0627\u062f\u06cc\u0627\u0646 \u0628\u0631\u0627\u06cc \u062c\u0628\u0631\u0627\u0646 \u0633\u0631\u06cc\u0639 \u0648 \u062f\u0642\u06cc\u0642 \u062d\u0631\u06a9\u062a \u0633\u0631 \u062f\u0631 CT \u067e\u0631\u062a\u0648 \u0645\u062e\u0631\u0648\u0637\u06cc"},"content":{"rendered":"<table class=\"table table-striped table-hover\">\n<tbody>\n<tr>\n<td>\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\n<td>A gradient-based approach to fast and accurate head motion compensation in cone-beam CT<\/td>\n<\/tr>\n<tr>\n<td>\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n<td>\u0645\u0642\u0627\u0644\u0647 \u06cc\u06a9 \u0631\u0648\u06cc\u06a9\u0631\u062f \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 \u06af\u0631\u0627\u062f\u06cc\u0627\u0646 \u0628\u0647 \u062c\u0628\u0631\u0627\u0646 \u062d\u0631\u06a9\u062a \u0633\u0631\u06cc\u0639 \u0648 \u062f\u0642\u06cc\u0642 \u0633\u0631 \u062f\u0631 CT \u0645\u062e\u0631\u0648\u0637<\/td>\n<\/tr>\n<tr>\n<td>\u0646\u0648\u06cc\u0633\u0646\u062f\u06af\u0627\u0646 <\/td>\n<td>Mareike Thies, Fabian Wagner, Noah Maul, Haijun Yu, Manuela Meier, Linda-Sophie Schneider, Mingxuan Gu, Siyuan Mei, Lukas Folle, Andreas Maier<\/td>\n<\/tr>\n<tr>\n<td>\u0632\u0628\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 <\/td>\n<td>\u0627\u0646\u06af\u0644\u06cc\u0633\u06cc<\/td>\n<\/tr>\n<tr>\n<td>\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647: <\/td>\n<td>PDF<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a<\/td>\n<td>15<\/td>\n<\/tr>\n<tr>\n<td>\u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u0648\u0636\u0648\u0639\u0627\u062a  <\/td>\n<td>Image and Video Processing,Computer Vision and Pattern Recognition,\u067e\u0631\u062f\u0627\u0632\u0634 \u062a\u0635\u0648\u06cc\u0631 \u0648 \u0648\u06cc\u062f\u06cc\u0648 \u060c \u0686\u0634\u0645 \u0627\u0646\u062f\u0627\u0632 \u0631\u0627\u06cc\u0627\u0646\u0647 \u0648 \u062a\u0634\u062e\u06cc\u0635 \u0627\u0644\u06af\u0648\u06cc \u060c<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0648\u0636\u06cc\u062d\u0627\u062a    <\/td>\n<td>Submitted 17 January, 2024; originally announced January 2024. , Comments: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc    <\/td>\n<td>\u0627\u0631\u0633\u0627\u0644 \u0634\u062f\u0647 17 \u0698\u0627\u0646\u0648\u06cc\u0647 2024 \u061b\u062f\u0631 \u0627\u0628\u062a\u062f\u0627 \u0698\u0627\u0646\u0648\u06cc\u0647 2024 \u0627\u0639\u0644\u0627\u0645 \u0634\u062f \u060c \u0646\u0638\u0631\u0627\u062a: \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0628\u0631\u0627\u06cc \u0627\u0646\u062a\u0634\u0627\u0631 \u0627\u062d\u062a\u0645\u0627\u0644\u06cc \u0628\u0647 IEEE \u0627\u0631\u0633\u0627\u0644 \u0634\u062f\u0647 \u0627\u0633\u062a.\u062d\u0642 \u0686\u0627\u067e \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0628\u062f\u0648\u0646 \u0627\u0637\u0644\u0627\u0639 \u0642\u0628\u0644\u06cc \u0645\u0646\u062a\u0642\u0644 \u0634\u0648\u062f \u060c \u067e\u0633 \u0627\u0632 \u0622\u0646 \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0627\u06cc\u0646 \u0646\u0633\u062e\u0647 \u062f\u06cc\u06af\u0631 \u062f\u0631 \u062f\u0633\u062a\u0631\u0633 \u0646\u0628\u0627\u0634\u062f<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u0686\u06a9\u06cc\u062f\u0647<\/h2>\n<p style=\"direction:ltr;\">Cone-beam computed tomography (CBCT) systems, with their portability, present a promising avenue for direct point-of-care medical imaging, particularly in critical scenarios such as acute stroke assessment. However, the integration of CBCT into clinical workflows faces challenges, primarily linked to long scan duration resulting in patient motion during scanning and leading to image quality degradation in the reconstructed volumes. This paper introduces a novel approach to CBCT motion estimation using a gradient-based optimization algorithm, which leverages generalized derivatives of the backprojection operator for cone-beam CT geometries. Building on that, a fully differentiable target function is formulated which grades the quality of the current motion estimate in reconstruction space. We drastically accelerate motion estimation yielding a 19-fold speed-up compared to existing methods. Additionally, we investigate the architecture of networks used for quality metric regression and propose predicting voxel-wise quality maps, favoring autoencoder-like architectures over contracting ones. This modification improves gradient flow, leading to more accurate motion estimation. The presented method is evaluated through realistic experiments on head anatomy. It achieves a reduction in reprojection error from an initial average of 3mm to 0.61mm after motion compensation and consistently demonstrates superior performance compared to existing approaches. The analytic Jacobian for the backprojection operation, which is at the core of the proposed method, is made publicly available. In summary, this paper contributes to the advancement of CBCT integration into clinical workflows by proposing a robust motion estimation approach that enhances efficiency and accuracy, addressing critical challenges in time-sensitive scenarios.<\/p>\n<h2>\u0686\u06a9\u06cc\u062f\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc (\u062a\u0631\u062c\u0645\u0647 \u0645\u0627\u0634\u06cc\u0646\u06cc)<\/h2>\n<p>\u0633\u06cc\u0633\u062a\u0645 \u0647\u0627\u06cc \u062a\u0648\u0645\u0648\u06af\u0631\u0627\u0641\u06cc \u06a9\u0627\u0645\u067e\u06cc\u0648\u062a\u0631\u06cc \u0645\u062e\u0631\u0648\u0637\u06cc (CBCT) \u060c \u0628\u0627 \u0642\u0627\u0628\u0644\u06cc\u062a \u062d\u0645\u0644 \u0622\u0646\u0647\u0627 \u060c \u06cc\u06a9 \u062e\u06cc\u0627\u0628\u0627\u0646 \u0627\u0645\u06cc\u062f\u0648\u0627\u0631\u06a9\u0646\u0646\u062f\u0647 \u0631\u0627 \u0628\u0631\u0627\u06cc \u062a\u0635\u0648\u06cc\u0631\u0628\u0631\u062f\u0627\u0631\u06cc \u0645\u0633\u062a\u0642\u06cc\u0645 \u0627\u0632 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\u0628\u06cc\u0645\u0627\u0631 \u062f\u0631 \u0637\u0648\u0644 \u0627\u0633\u06a9\u0646 \u0645\u0631\u062a\u0628\u0637 \u0627\u0633\u062a \u0648 \u0645\u0646\u062c\u0631 \u0628\u0647 \u062a\u062e\u0631\u06cc\u0628 \u06a9\u06cc\u0641\u06cc\u062a \u062a\u0635\u0648\u06cc\u0631 \u062f\u0631 \u062d\u062c\u0645 \u0628\u0627\u0632\u0633\u0627\u0632\u06cc \u0634\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f.\u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u06cc\u06a9 \u0631\u0648\u06cc\u06a9\u0631\u062f \u062c\u062f\u06cc\u062f \u0628\u0631\u0627\u06cc \u0628\u0631\u0622\u0648\u0631\u062f \u062d\u0631\u06a9\u062a CBCT \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06cc\u06a9 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0628\u0647\u06cc\u0646\u0647 \u0633\u0627\u0632\u06cc \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 \u06af\u0631\u0627\u062f\u06cc\u0627\u0646 \u060c \u06a9\u0647 \u0645\u0634\u062a\u0642\u0627\u062a \u062a\u0639\u0645\u06cc\u0645 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\u0645\u06cc \u0634\u0648\u062f \u060c \u0628\u0647 \u067e\u06cc\u0634\u0631\u0641\u062a \u0627\u062f\u063a\u0627\u0645 CBCT \u062f\u0631 \u06af\u0631\u062f\u0634 \u06a9\u0627\u0631 \u0628\u0627\u0644\u06cc\u0646\u06cc \u06a9\u0645\u06a9 \u0645\u06cc \u06a9\u0646\u062f \u0648 \u0628\u0647 \u0686\u0627\u0644\u0634 \u0647\u0627\u06cc \u0645\u0647\u0645 \u062f\u0631 \u0633\u0646\u0627\u0631\u06cc\u0648\u0647\u0627\u06cc \u062d\u0633\u0627\u0633 \u0632\u0645\u0627\u0646 \u0645\u06cc \u067e\u0631\u062f\u0627\u0632\u062f.<\/p>\n<table class=\"table table-bordered table-primary\">\r\n<tr>\r\n\t<td>\r\n\t\u062a\u0648\u062c\u0647 \u06a9\u0646\u06cc\u062f \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0632\u0628\u0627\u0646 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc \u0627\u0633\u062a.\r\n\t<\/td>\r\n<\/tr>\r\n\t\r\n<tr>\r\n\t<td>\r\n     \u0628\u0631\u0627\u06cc \u0633\u0641\u0627\u0631\u0634 \u062a\u0631\u062c\u0645\u0647 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0647 \u06cc\u06a9\u06cc \u0627\u0632 \u0631\u0648\u0634 \u0647\u0627\u06cc \u062a\u0645\u0627\u0633\u060c \u067e\u06cc\u0627\u0645\u06a9\u060c \u062a\u0644\u06af\u0631\u0627\u0645 \u0648 \u06cc\u0627 \u0648\u0627\u062a\u0633 \u0627\u067e \u0628\u0627 \u0634\u0645\u0627\u0631\u0647 \u0632\u06cc\u0631 \u062a\u0645\u0627\u0633 \u0628\u06af\u06cc\u0631\u06cc\u062f:\r\n\t<br \/><br \/>\r\n\t09395106248\r\n\t\t\r\n\t\t\r\n\t<br \/><br \/>\r\n\t\t\u062a\u0648\u062c\u0647 \u06a9\u0646\u06cc\u062f \u06a9\u0647 \u0634\u0631\u0627\u06cc\u0637 \u062a\u0631\u062c\u0645\u0647 \u0628\u0647 \u0635\u0648\u0631\u062a \u0632\u06cc\u0631 \u0627\u0633\u062a:\r\n\t\t<br \/>\r\n\t\t<ul>\r\n\t\t<li>\r\n\t\t\u0642\u06cc\u0645\u062a \u0647\u0631 \u0635\u0641\u062d\u0647 \u062a\u0631\u062c\u0645\u0647 \u062f\u0631 \u062d\u0627\u0644 \u062d\u0627\u0636\u0631 40 \u0647\u0632\u0627\u0631 \u062a\u0648\u0645\u0627\u0646 \u0645\u06cc \u0628\u0627\u0634\u062f. \r\n\t\t<\/li>\r\n\t\t<li>\r\n\t\t\u062a\u062d\u0648\u06cc\u0644 \u0645\u0642\u0627\u0644\u0647 \u062a\u0631\u062c\u0645\u0647 \u0634\u062f\u0647 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f \u0645\u06cc \u0628\u0627\u0634\u062f.\r\n\t\t<\/li>\r\n\t\t<li>\r\n\t\t\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 \u062f\u0631 \u0635\u0648\u0631\u062a \u062f\u0627\u0634\u062a\u0646 \u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a \u0639\u0627\u062f\u06cc \u0628\u06cc\u0646 3 \u062a\u0627 5 \u0631\u0648\u0632 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.\r\n\t\t<\/li>\r\n\t\t\r\n\t\t<li>\r\n\t\t\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627 \u0645\u06cc \u0628\u0627\u0634\u062f. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.\r\n\t\t<\/li>\r\n\t\t<li>\r\n\t\t\u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.\r\n\t\t<\/li>\r\n\t<\/td>\r\n\t\r\n\t\r\n<\/tr>\r\n<\/table>\r\n\r\n\r\n\n","protected":false},"excerpt":{"rendered":"<p>\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc A gradient-based approach to fast and accurate head motion compensation in cone-beam CT \u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 [&hellip;]<\/p>\n","protected":false},"featured_media":27,"comment_status":"open","ping_status":"closed","template":"","meta":{"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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