biostudies-literatureUnknownVornehm MNIBIB NIH HHSNHLBI NIH HHSCardiovascular magnetic resonance imaging is a powerful diagnostic tool for assessing cardiac structure and function. However, traditional breath-held imaging protocols pose challenges for patients with arrhythmias or limited breath-holding capacity. This work aims to overcome these limitations by developing a reconstruction framework that enables high-quality imaging in free-breathing conditions for various dynamic cardiac MRI protocols. Multi-Dynamic Deep Image Prior (M-DIP), a novel unsupervised reconstruction framework for accelerated real-time cardiac MRI, is introduced. To capture contrast or content variation, M-DIP first employs a spatial dictionary to synthesize a time-dependent intermediate image. Then, this intermediate image is further refined using time-dependent deformation fArXivarXiv:2412.04639v2https://www.ebi.ac.uk/biostudies/studies/S-EPMC11643223biostudies-literatureMulti-dynamic deep image prior for cardiac MRI.PMC11643223R01 HL148103R01 EB029957Ahmad RKnoll FVornehm MHan YSultan MAChen CArshad SMfalseMulti-dynamic deep image prior for cardiac MRI.Cardiovascular magnetic resonance imaging is a powerful diagnostic tool for assessing cardiac structure and function. However, traditional breath-held imaging protocols pose challenges for patients with arrhythmias or limited breath-holding capacity. This work aims to overcome these limitations by developing a reconstruction framework that enables high-quality imaging in free-breathing conditions for various dynamic cardiac MRI protocols. Multi-Dynamic Deep Image Prior (M-DIP), a novel unsupervised reconstruction framework for accelerated real-time cardiac MRI, is introduced. To capture contrast or content variation, M-DIP first employs a spatial dictionary to synthesize a time-dependent intermediate image. Then, this intermediate image is further refined using time-dependent deformation f2025-01-01T00:00:00Z2025 Jul2025-08-14T03:05:31.235Z2025-08-14T03:05:31.235ZS-EPMC1164322339679265