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Standard protocols, however, can be inefficient, facing challenges due to significant operator and patient involvement. Although the free\u2010running framework (FRF) addresses these challenges, the potential of the extensive data it collects remains underutilized. Therefore, we propose to leverage the large amount of data collected by incorporating interbin cardiac motion compensation into FRF (FRF\u2010MC) to improve both image quality and LVEF measurement accuracy, while reducing the sensitivity to user\u2010defined regularization parameters.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>FRF\u2010MC consists of several steps: data acquisition, self\u2010gating signal extraction, deformation field estimations, and motion\u2010resolved reconstruction with interbin cardiac motion compensation. FRF\u2010MC was compared with the original 5D\u2010FRF method using LVEF and several image\u2010quality metrics. The cardiac regularization weight () was optimized for both methods by maximizing image quality without compromising LVEF measurement accuracy. Evaluations were performed in numerical simulations and in 9 healthy participants. In vivo images were assessed by blinded expert reviewers and compared with reference standard 2D\u2010cine images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Both in silico and in vivo results revealed that FRF\u2010MC outperformed FRF in terms of image quality and LVEF accuracy. FRF\u2010MC reduced temporal blurring, preserving detailed anatomy even at higher cardiac regularization weights, and led to more accurate LVEF measurements. Optimized  produced accurate LVEF for both methods compared with the 2D\u2010cine reference (FRF\u2010MC: 0.59% [\u22127.2%, 6.0%],\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.47; FRF: 0.86% [\u22128.5%, 6.7%],\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.36), but FRF\u2010MC resulted in superior image quality (FRF\u2010MC: 2.89\u2009\u00b1\u20090.58, FRF: 2.11\u2009\u00b1\u20090.47;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u200910\n                      <jats:sup>\u22123<\/jats:sup>\n                      ).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Incorporating interbin cardiac motion compensation significantly improved image quality, supported higher cardiac regularization weights without compromising LVEF measurement accuracy, and reduced sensitivity to user\u2010defined regularization parameters.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.30323","type":"journal-article","created":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T08:32:19Z","timestamp":1728549139000},"page":"975-992","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["High on sparsity: Interbin compensation of cardiac motion for improved assessment of left\u2010ventricular function using\n                    <scp>5D<\/scp>\n                    whole\u2010heart\n                    <scp>MRI<\/scp>"],"prefix":"10.1002","volume":"93","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4347-8613","authenticated-orcid":false,"given":"J\u00e9r\u00f4me","family":"Yerly","sequence":"first","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology Lausanne University Hospital and University of Lausanne  Lausanne Vaud 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