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However, traditional breath\u2010held imaging protocols pose challenges for patients with arrhythmias or limited breath\u2010holding capacity. This work aims to overcome these limitations by developing a reconstruction framework that enables high\u2010quality imaging in free\u2010breathing conditions for various dynamic cardiac MRI protocols.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Multi\u2010Dynamic Deep Image Prior (M\u2010DIP), a novel unsupervised reconstruction framework for accelerated real\u2010time cardiac MRI, is introduced. To capture contrast or content variation, M\u2010DIP first employs a spatial dictionary to synthesize a time\u2010dependent intermediate image. Then, this intermediate image is further refined using time\u2010dependent deformation fields that model cardiac and respiratory motion. Unlike prior DIP\u2010based methods, M\u2010DIP simultaneously captures physiological motion and frame\u2010to\u2010frame content variations, making it applicable to a wide range of dynamic applications.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We validate M\u2010DIP using simulated MRXCAT cine phantom data as well as free\u2010breathing real\u2010time cine, single\u2010shot late gadolinium enhancement (LGE), and first\u2010pass perfusion data from clinical patients. Comparative analyses against state\u2010of\u2010the\u2010art supervised and unsupervised approaches demonstrate M\u2010DIP's performance and versatility. M\u2010DIP achieved better image quality metrics on phantom data, higher reader scores on in\u2010vivo cine and LGE data, and comparable scores on in\u2010vivo perfusion data relative to another DIP\u2010based approach.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>M\u2010DIP enables high\u2010quality reconstructions of real\u2010time free\u2010breathing cardiac MRI without requiring external training data. Its ability to model physiological motion and content variations makes it a promising approach for various dynamic imaging applications.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mrm.70000","type":"journal-article","created":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T08:00:32Z","timestamp":1753171232000},"page":"2668-2679","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Multi\u2010dynamic deep image prior for cardiac MRI"],"prefix":"10.1002","volume":"94","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-5687-0563","authenticated-orcid":false,"given":"Marc","family":"Vornehm","sequence":"first","affiliation":[{"name":"Department Artificial Intelligence in Biomedical Engineering Friedrich\u2010Alexander\u2010Universit\u00e4t Erlangen\u2010N\u00fcrnberg  Erlangen Germany"},{"name":"Department of Biomedical Engineering The Ohio State University  Columbus Ohio USA"},{"name":"Research &amp; Clinical Translation, 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