{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T06:21:24Z","timestamp":1769840484627,"version":"3.49.0"},"reference-count":33,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T00:00:00Z","timestamp":1628553600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["T32CA009695"],"award-info":[{"award-number":["T32CA009695"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R21EB030080"],"award-info":[{"award-number":["R21EB030080"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2021,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Metal artifact remains a challenge in cone\u2010beam CT images. Many image domain\u2010based segmentation methods have been proposed for metal artifact reduction (MAR), which require two\u2010pass reconstruction. Such methods first segment metal from a first\u2010pass reconstruction and then forward\u2010project the metal mask to identify them in projections. These methods work well in general but are limited when the metal is outside the scan field\u2010of\u2010view (FOV) or when the metal is moving during the scan. In the former, even reconstructing with a larger FOV does not guarantee a good estimate of metal location in the projections; and in the latter, the metal location in each projection is difficult to identify due to motion. Single\u2010pass methods that detect metal in single\u2010energy projections have also been developed, but often have imperfect metal detection that leads to residual artifacts. In this work, we develop a MAR method using a dual\u2010layer (DL) flat panel detector, which improves performance for single\u2010pass reconstruction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this work, we directly detect metal objects in projections using dual\u2010energy (DE) imaging that generates material\u2010specific images (e.g., soft tissue and bone), where the metal stands out in bone images when nonuniform soft tissue background is removed. Metal is detected via simple thresholding, and entropy filtration is further applied to remove false\u2010positive detections. A DL detector provides DE images with superior temporal and spatial registration and was used to perform the task. Scatter correction was first performed on DE raw projections to improve the accuracy of material decomposition. One phantom mimicking a liver biopsy setup and a cadaver head were used to evaluate the metal reduction performance of the proposed method and compared with that of a standard two\u2010pass reconstruction, a previously published sinogram\u2010based method using a Markov random field (MRF) model, and a single\u2010pass projection\u2010domain method using single\u2010energy imaging. The phantom has a liver steering setup placed in a hollow chest phantom, with embedded metal and a biopsy needle crossing the phantom boundary. The cadaver head has dental fillings and a metal tag attached to its surface. The identified metal regions in each projection were corrected by interpolation using surrounding pixels, and the images were reconstructed using filtered backprojection.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Our current approach removes metal from the projections, which is robust to FOV truncation during imaging acquisition. In case of FOV truncation, the method outperformed the two\u2010pass reconstruction method. The proposed method using DE renders better accuracy in metal segmentation than the MRF method and single\u2010energy method, which were prone to false\u2010positive errors that cause additional streaks. For the liver steering phantom, the average spatial nonuniformity was reduced from 0.127 in uncorrected images to 0.086 using a standard two\u2010pass reconstruction and to 0.077 using the proposed method. For the cadaver head, the average standard deviation within selected soft tissue regions () was reduced from 209.1 HU in uncorrected images to 69.1 HU using a standard two\u2010pass reconstruction and to 46.8 HU using our proposed method. The proposed method reduced the processing time by 31% as compared with the two\u2010pass method.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>We proposed a MAR method that directly detects metal in the projection domain using DE imaging, which is robust to truncation and superior to that of single\u2010energy imaging. The method requires only a single\u2010pass reconstruction that substantially reduces processing time compared with the standard two\u2010pass metal reduction method.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.15131","type":"journal-article","created":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T08:21:30Z","timestamp":1628583690000},"page":"6482-6496","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Single\u2010pass metal artifact reduction using a dual\u2010layer flat panel detector"],"prefix":"10.1002","volume":"48","author":[{"given":"Linxi","family":"Shi","sequence":"first","affiliation":[{"name":"Department of Radiology Stanford University Stanford CA USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"N. 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