{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T16:10:58Z","timestamp":1783786258063,"version":"3.55.0"},"reference-count":44,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:00:00Z","timestamp":1771372800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:00:00Z","timestamp":1771372800000},"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"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance in Med"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>\n                      To develop a deep learning\u2010based auto\u2010navigation technique for free\u2010breathing golden\u2010angle radial MRI named RANGR (\n                      <jats:italic>R<\/jats:italic>\n                      espiratory\n                      <jats:italic>A<\/jats:italic>\n                      uto\u2010\n                      <jats:italic>N<\/jats:italic>\n                      avigator for\n                      <jats:italic>G<\/jats:italic>\n                      olden\u2010angle\n                      <jats:italic>R<\/jats:italic>\n                      adial free\u2010breathing abdominal MRI).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      RANGR computes a one\u2010dimensional (1D) respiratory motion signal from 1D projections along the superior\u2013inferior dimension (\n                      <jats:italic>z<\/jats:italic>\n                      ) extracted directly from the acquired golden\u2010angle stack\u2010of\u2010stars k\u2010space data. The motion signal is used to retrospectively sort k\u2010space data into different undersampled motion states, which are reconstructed using Movienet, a neural network developed for dynamic reconstruction. RANGR was trained using PCA (Principal Component Analysis) as a reference in cases where PCA was successful. The performance of RANGR is evaluated against PCA using a dynamic phantom with programmable motion waveforms on a 1.5\u2009T MR\u2010Linac system and free\u2010breathing imaging of patients with abdominal tumors. In vivo image quality was qualitatively scored by body radiologists.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      For phantom studies, RANGR can track motion with less than 1\u2009mm error with respect to the ground\u2010truth. For in vivo studies, RANGR generalizes to cases where PCA failed due to limited liver coverage and\/or the presence of high intensity regions outside the liver dome. RANGR outscores PCA on all qualitative image criteria as evaluated on a 5\u2010point Likert scale by two expert body radiologists (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05). RANGR estimates motion faster than PCA on GPU (1.7\u2009\u00b1\u20090.3 vs. 168.7\u2009\u00b1\u2009172.4\u2009ms,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.005). The total time from motion estimation to Movienet reconstruction is 2.91\u2009\u00b1\u20091.07\u2009s.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>RANGR presents a robust auto\u2010navigation solution based on deep learning for free\u2010breathing MRI using golden\u2010angle radial MRI acquisition.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.70313","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T03:59:31Z","timestamp":1771473571000},"page":"259-272","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep Learning\u2010Based Auto\u2010Navigation for Free\u2010Breathing Golden\u2010Angle Radial\n                    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