{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T16:47:12Z","timestamp":1791046032401,"version":"4.1.0"},"reference-count":61,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T00:00:00Z","timestamp":1631491200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["R01HL153594"],"award-info":[{"award-number":["R01HL153594"]}],"id":[{"id":"10.13039\/100000050","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:p><jats:bold>Background<\/jats:bold>: Sparse\u2010view CT image reconstruction problems encountered in dynamic CT acquisitions are technically challenging. Recently, many deep learning strategies have been proposed to reconstruct CT images from sparse\u2010view angle acquisitions showing promising results. However, two fundamental problems with these deep learning reconstruction methods remain to be addressed: (1) limited reconstruction accuracy for individual patients and (2) limited generalizability for patient statistical\u00a0cohorts.<\/jats:p><jats:p><jats:bold>Purpose<\/jats:bold>: The purpose of this work is to address the previously mentioned challenges in current deep learning\u00a0methods.<\/jats:p><jats:p><jats:bold>Methods<\/jats:bold>: A method that combines a deep learning strategy with prior image constrained compressed sensing (PICCS) was developed to address these two problems. In this method, the sparse\u2010view CT data were reconstructed by the conventional filtered backprojection (FBP) method first, and then processed by the trained deep neural network to eliminate streaking artifacts. The outputs of the deep learning architecture were then used as the needed prior image in PICCS to reconstruct the image. If the noise level from the PICCS reconstruction is not satisfactory, another light duty deep neural network can then be used to reduce noise level. Both extensive numerical simulation data and human subject data have been used to quantitatively and qualitatively assess the performance of the proposed DL\u2010PICCS method in terms of reconstruction accuracy and\u00a0generalizability.<\/jats:p><jats:p><jats:bold>Results<\/jats:bold>: Extensive evaluation studies have demonstrated that: (1) quantitative reconstruction accuracy of DL\u2010PICCS for individual patient is improved when it is compared with the deep learning methods and CS\u2010based methods; (2) the false\u2010positive lesion\u2010like structures and false negative missing anatomical structures in the deep learning approaches can be effectively eliminated in the DL\u2010PICCS reconstructed images; and (3) DL\u2010PICCS enables a deep learning scheme to relax its working conditions to enhance its\u00a0generalizability.<\/jats:p><jats:p><jats:bold>Conclusions<\/jats:bold>: DL\u2010PICCS offers a promising opportunity to achieve personalized reconstruction with improved reconstruction accuracy and enhanced generalizability.<\/jats:p>","DOI":"10.1002\/mp.15183","type":"journal-article","created":{"date-parts":[[2021,8,30]],"date-time":"2021-08-30T06:21:34Z","timestamp":1630304494000},"page":"5765-5781","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Accurate and robust sparse\u2010view angle CT image reconstruction using deep learning and prior image constrained compressed sensing (DL\u2010PICCS)"],"prefix":"10.1002","volume":"48","author":[{"given":"Chengzhu","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Medical Physics University of Wisconsin School of Medicine and Public Health  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinsheng","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Medical Physics University of Wisconsin School of Medicine and Public Health  Madison Wisconsin 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