{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T16:25:25Z","timestamp":1779380725434,"version":"3.53.1"},"reference-count":24,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,2,18]],"date-time":"2024-02-18T00:00:00Z","timestamp":1708214400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["R01 HL148210"],"award-info":[{"award-number":["R01 HL148210"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Artifacts from implantable cardioverter defibrillators (ICDs) are a challenge to magnetic resonance imaging (MRI)\u2010guided radiotherapy (MRgRT).<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>This study tested an unsupervised generative adversarial network to mitigate ICD artifacts in balanced steady\u2010state free precession (bSSFP) cine MRIs and improve image quality and tracking performance for MRgRT.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Fourteen healthy volunteers (Group A) were scanned on a 0.35 T MRI\u2010Linac with and without an MR conditional ICD taped to their left pectoral to simulate an implanted ICD. bSSFP MRI data from 12 of the volunteers were used to train a CycleGAN model to reduce ICD artifacts. The data from the remaining two volunteers were used for testing. In addition, the dataset was reorganized three times using a Leave\u2010One\u2010Out scheme. Tracking metrics [Dice similarity coefficient (DSC), target registration error (TRE), and 95 percentile Hausdorff distance (95% HD)] were evaluated for whole\u2010heart contours. Image quality metrics [normalized root mean square error (nRMSE), peak signal\u2010to\u2010noise ratio (PSNR), and multiscale structural similarity (MS\u2010SSIM) scores] were evaluated. The technique was also tested qualitatively on three additional ICD datasets (Group B) including a patient with an implanted ICD.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>For the whole\u2010heart contour with CycleGAN reconstruction: 1) Mean DSC rose from 0.910 to 0.935; 2) Mean TRE dropped from 4.488 to 2.877\u00a0mm; and 3) Mean 95% HD dropped from 10.236 to 7.700\u00a0mm. For the whole\u2010body slice with CycleGAN reconstruction: 1) Mean nRMSE dropped from 0.644 to 0.420; 2) Mean MS\u2010SSIM rose from 0.779 to 0.819; and 3) Mean PSNR rose from 18.744 to 22.368. The three Group B datasets evaluated qualitatively displayed a reduction in ICD artifacts in the heart.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>CycleGAN\u2010generated reconstructions significantly improved both tracking and image quality metrics when used to mitigate artifacts from ICDs.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.14304","type":"journal-article","created":{"date-parts":[[2024,2,18]],"date-time":"2024-02-18T17:39:20Z","timestamp":1708277960000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Technical note: Minimizing CIED artifacts on a 0.35 T MRI\u2010Linac using deep learning"],"prefix":"10.1002","volume":"25","author":[{"given":"Austen N.","family":"Curcuru","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Washington University in St. Louis  St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2568-247X","authenticated-orcid":false,"given":"Deshan","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Duke University  Durham North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyu","family":"An","sequence":"additional","affiliation":[{"name":"Departments of Radiology Biomedical Engineering and Neurology Washington University in St. Louis  St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Phillip S.","family":"Cuculich","sequence":"additional","affiliation":[{"name":"Departments of Cardiovascular Medicine and Radiation Oncology Washington University in St. Louis  St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clifford G.","family":"Robinson","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Washington University in St. Louis  St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H. Michael","family":"Gach","sequence":"additional","affiliation":[{"name":"Departments of Radiation Oncology Radiology and Biomedical Engineering Washington University in St. Louis  St. Louis Missouri 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