{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T05:21:12Z","timestamp":1788153672323,"version":"build-2803163510"},"reference-count":33,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01\u2010CA255661"],"award-info":[{"award-number":["R01\u2010CA255661"]}],"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":["R01\u2010CA244532"],"award-info":[{"award-number":["R01\u2010CA244532"]}],"id":[{"id":"10.13039\/100000002","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":[[2026,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop and evaluate an automated clinical prototype for a 1\u2010min free\u2010breathing T1\u2010weighted 3D MRI and a 2.25\u2010min 4D MRI utilizing radial k\u2010space acquisition and deep learning (DL) auto\u2010navigation and reconstruction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The clinical prototype was deployed on 3\u2009T GE Healthcare scanners, using the GE DISCO\u2010Star (radial golden\u2010angle stack\u2010of\u2010stars) pulse sequence, DL auto\u2010navigation (RANGR), DL reconstruction (Movienet), and vendor\u2010specific image processing to efficiently generate DICOM files. The system automatically collects raw data, transmits the data to an external high\u2010performance computer, performs image reconstruction, and generates DICOM files. A customized version of the Movienet network was trained, using compressed sensing references, to achieve 2.25\u2010fold and 2\u2010fold acquisition acceleration for 3D and 4D MRI, respectively. The prototype was evaluated on 50 patients (ages: 8\u201387) with TE\u2009=\u20091.46\u20131.6\u2009ms, TR\u2009=\u20093.2\u20133.4\u2009ms, flip angle\u2009=\u200912\u00b0, in\u2010plane resolution\u2009=\u20091.17\u20131.64\u2009mm, and slice thickness\u2009=\u20094\u2009mm. Image quality was assessed qualitatively by three expert radiologists, who compared Movienet to conventional vendor methods, followed by a statistical analysis using Wilcoxon signed\u2010rank tests.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The Movienet prototype demonstrated remarkable efficiency, requiring only 90\u2009s of GPU and 4\u2009min on a CPU computation for 3D reconstruction, while exhibiting better performance, characterized by reduced streaking artifacts and about one\u2010point improvement in image quality on a five\u2010point scale compared to vendor technology. For 4D reconstructions, reconstruction time increased by 20\u2009s (GPU, +1\u2009min CPU) while maintaining comparable quality metrics. For all metrics, the differences were statistically significant (all\n                      <jats:italic>p<\/jats:italic>\n                      \u2010values &lt;\u20090.0001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The Movienet prototype significantly enhances motion robustness and acquisition speed in abdominal MRI, offering a transformative approach for clinical applications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.70505","type":"journal-article","created":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T04:40:43Z","timestamp":1783744843000},"page":"2266-2277","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>3D<\/scp>\n                    and\n                    <scp>4D<\/scp>\n                    Free\u2010Breathing Abdominal\n                    <scp>T1<\/scp>\n                    \u2010Weighted\n                    <scp>MRI<\/scp>\n                    in Clinical Practice Using Deep Learning Auto\u2010Navigation and Reconstruction"],"prefix":"10.1002","volume":"96","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6000-3380","authenticated-orcid":false,"given":"Victor","family":"Murray","sequence":"first","affiliation":[{"name":"Department of Medical Physics Memorial Sloan Kettering Cancer Center  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Wen","sequence":"additional","affiliation":[{"name":"GE HealthCare, ASL East  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Subin","family":"Erattakulangara","sequence":"additional","affiliation":[{"name":"Department of Medical Physics Memorial Sloan Kettering Cancer Center  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oguz","family":"Akin","sequence":"additional","affiliation":[{"name":"Department of Radiology Memorial Sloan Kettering Cancer Center  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6554-0310","authenticated-orcid":false,"given":"Richard","family":"Do","sequence":"additional","affiliation":[{"name":"Department of Radiology Memorial Sloan Kettering Cancer Center  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerald","family":"Behr","sequence":"additional","affiliation":[{"name":"Department of Radiology Memorial Sloan Kettering Cancer Center  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhigang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arnaud","family":"Guidon","sequence":"additional","affiliation":[{"name":"GE HealthCare, ASL East  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3782-4930","authenticated-orcid":false,"given":"Ricardo","family":"Otazo","sequence":"additional","affiliation":[{"name":"Department of Medical Physics Memorial Sloan Kettering Cancer Center  New York New York USA"},{"name":"Department of Radiology Memorial Sloan Kettering Cancer Center  New York New York 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