{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T20:02:48Z","timestamp":1787169768750,"version":"build-2736575974"},"reference-count":65,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000925","name":"National Health and Medical Research Council","doi-asserted-by":"publisher","award":["1132471"],"award-info":[{"award-number":["1132471"]}],"id":[{"id":"10.13039\/501100000925","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance in Med"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>MRI is increasingly utilized for image\u2010guided radiotherapy due to its outstanding soft\u2010tissue contrast and lack of ionizing radiation. However, geometric distortions caused by gradient nonlinearities (GNLs) limit anatomical accuracy, potentially compromising the quality of tumor treatments. In addition, slow MR acquisition and reconstruction limit the potential for effective image guidance. Here, we demonstrate a deep learning\u2010based method that rapidly reconstructs distortion\u2010corrected images from raw k\u2010space data for MR\u2010guided radiotherapy applications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We leverage recent advances in interpretable unrolling networks to develop a Distortion\u2010Corrected Reconstruction Network (DCReconNet) that applies convolutional neural networks (CNNs) to learn effective regularizations and nonuniform fast Fourier transforms for GNL\u2010encoding. DCReconNet was trained on a public MR brain dataset from 11 healthy volunteers for fully sampled and accelerated techniques, including parallel imaging (PI) and compressed sensing (CS). The performance of DCReconNet was tested on phantom, brain, pelvis, and lung images acquired on a 1.0T MRI\u2010Linac. The DCReconNet, CS\u2010, PI\u2010and UNet\u2010based reconstructed image quality was measured by structural similarity (SSIM) and RMS error (RMSE) for numerical comparisons. The computation time and residual distortion for each method were also reported.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Imaging results demonstrated that DCReconNet better preserves image structures compared to CS\u2010 and PI\u2010based reconstruction methods. DCReconNet resulted in the highest SSIM (0.95 median value) and lowest RMSE (&lt;0.04) on simulated brain images with four times acceleration. DCReconNet is over 10\u2010times faster than iterative, regularized reconstruction methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>DCReconNet provides fast and geometrically accurate image reconstruction and has the potential for MRI\u2010guided radiotherapy applications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.29684","type":"journal-article","created":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T06:05:45Z","timestamp":1682921145000},"page":"963-977","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["<scp>Distortion\u2010corrected<\/scp>\n                    image reconstruction with deep learning on an\n                    <scp>MRI\u2010Linac<\/scp>"],"prefix":"10.1002","volume":"90","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8758-420X","authenticated-orcid":false,"given":"Shanshan","family":"Shan","sequence":"first","affiliation":[{"name":"ACRF Image X Institute, Sydney School of Health Sciences, Faculty of Medicine and Health The University of Sydney  Sydney New South Wales Australia"},{"name":"State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD\u2010X), Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions Soochow University  Suzhou Jiangsu China"},{"name":"Department of Medical Physics Ingham Institute of Applied Medical Research  Liverpool New South Wales Australia"},{"name":"School of Information Technology and Electrical Engineering The University of Queensland  Brisbane Queensland Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3846-0999","authenticated-orcid":false,"given":"Yang","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Information Technology and Electrical Engineering The University of Queensland  Brisbane Queensland Australia"},{"name":"School of Computer Science and Engineering Central South University  Changsha Hunan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul Z. 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