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Cardiac MRI can non\u2010invasively quantify \u0394SO\n                      <jats:sub>2<\/jats:sub>\n                      with quantitative susceptibility mapping (QSM) using a prospective navigator gated 3D cartesian acquisition. However, this method suffers from long acquisition time and reduced robustness. Here, a free\u2010breathing cardiac QSM using spiral sampling and deep learning motion compensation is proposed.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A retrospective self\u2010gated stack\u2010of\u2010spirals multi\u2010echo gradient echo sequence is combined with implicit neural representation (INR) learning for image reconstruction. The self\u2010gating signals measure superior\u2013inferior cardiac and respiratory motion thus allowing k\u2010space binning. Using a physics\u2010informed signal model and the spatiotemporal coordinate input, INR infers motion fields as well as motion\u2010corrected water, fat, and field maps. Then, QSM and \u0394SO\n                      <jats:sub>2<\/jats:sub>\n                      are accordingly computed. Data were acquired in 10 healthy subjects. For comparison, a free\u2010breathing prospective navigator ECG\u2010triggered Cartesian acquisition (NAV) was performed.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      INR reconstructed motion\u2010corrected water, fat, R2* and field maps were successfully obtained in all subjects. INR\u2010QSM showed superior image quality (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.0067) and equivalent \u0394SO\n                      <jats:sub>2<\/jats:sub>\n                      measurement in the heart (\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.74,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001; 1.07%\u2009\u00b1\u20093.52% bias\/limits of agreement) compared to the reference NAV\u2010QSM.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study demonstrated the feasibility of INR for compensation of cardiac and respiratory motion in free\u2010breathing 3D cardiac QSM.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.70325","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T11:05:18Z","timestamp":1772967918000},"page":"227-237","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Feasibility of Implicit Neural Representation Learned Motion Compensation for\n                    <scp>3D<\/scp>\n                    Stack\u2010of\u2010Spirals Free\u2010Breathing Cardiac Quantitative Susceptibility Mapping"],"prefix":"10.1002","volume":"96","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4706-4808","authenticated-orcid":false,"given":"Jiahao","family":"Li","sequence":"first","affiliation":[{"name":"Meinig School of Biomedical Engineering Cornell University  Ithaca New York USA"},{"name":"Radiology Weill Cornell Medicine  New York New York 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