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However, current reconstruction methods typically process each session independently without using this rich temporal context. This work presents a novel concept of longitudinal dynamic MRI that incorporates patient\u2010specific prior images across multiple imaging sessions to enable higher acceleration than standard single\u2010session reconstruction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The feasibility of longitudinal dynamic MRI reconstruction was demonstrated using the 4D Golden\u2010angle RAdial Sparse Parallel (GRASP) technique. Multi\u2010session GRASP datasets were concatenated into an extended dynamic series and reconstructed using a low\u2010rank subspace approach. Imaging experiments were performed on eight subjects across three separate sessions to evaluate reconstruction performance under different conditions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Compared with single\u2010session reconstruction, longitudinal 4D GRASP reconstruction enabled higher acceleration and achieved superior image quality across all subjects, even with up to a 5\u2010fold reduction in scan duration. Longitudinal reconstruction also demonstrated robustness to inter\u2010session variations in anatomy, body contour, imaging interval, and fat content without compromising reconstruction fidelity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed longitudinal dynamic MRI framework leverages shared patient\u2010specific information across imaging sessions to improve reconstruction quality and accelerate data acquisition. This work represents a promising imaging paradigm for applications involving repeated dynamic MRI scans, where imaging may become more efficient as additional patient\u2010specific data accumulate over time.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.70611","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T05:03:28Z","timestamp":1790831008000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Longitudinal Dynamic\n                    <scp>MRI<\/scp>\n                    Through Multisession Joint Reconstruction Incorporating Patient\u2010Specific Prior Image Information"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6023-1033","authenticated-orcid":false,"given":"Jingjia","family":"Chen","sequence":"first","affiliation":[{"name":"Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology New York University Grossman School of Medicine  New York New York USA"},{"name":"Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology New York University Grossman School of Medicine  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hersh","family":"Chandarana","sequence":"additional","affiliation":[{"name":"Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology New York University Grossman School of Medicine  New York New York USA"},{"name":"Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology New York University Grossman School of Medicine  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel K.","family":"Sodickson","sequence":"additional","affiliation":[{"name":"Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology New York University Grossman School of Medicine  New York New York USA"},{"name":"Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology New York University Grossman School of Medicine  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8692-7645","authenticated-orcid":false,"given":"Li","family":"Feng","sequence":"additional","affiliation":[{"name":"Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology New York University Grossman School of Medicine  New York New York USA"},{"name":"Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology New York University Grossman School of Medicine  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.10171"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.1910380414"},{"issue":"5","key":"e_1_2_10_4_1","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1002\/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S","article-title":"SENSE: Sensitivity Encoding for Fast MRI","volume":"42","author":"Pruessmann K. 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