biostudies-literatureCurtis ADNatural Sciences & Engineering Research Council of Canada (NSERC)The Dean's Strategic Fund, Univesity of TorontoOntario Graduate Scholarship, Government of OntarioCanada Foundation for InnovationCIHR585-600https://www.ebi.ac.uk/biostudies/studies/S-EPMC12620177biostudies-literatureUnknown95(1)<h4>Purpose</h4>True real-time cardiac MRI (CMR), necessary for capturing live cardiac dynamics and imaging irregular cardiac rhythms, remains challenging. In this article, we move toward real-time CMR in multiple reconstruction frameworks via strategies to predict cardiac motion, improve computational efficiency, reduce artifacts, and preserve spatial resolution.<h4>Theory and methods</h4>A published predictive signal model (PMOT) for imaging irregular cardiac dynamics was modified (mPMOT) to enable efficient computation of state-transition matrices for predicting cardiac motion, as training PMOT is computationally expensive. A multi-rate Kalman filter framework was developed to enable computationally efficient reconstructions of high-resolution, large-matrix CMR datasets. ReconstructionsMagnetic resonance in medicinePredictive signal modeling and multi-rate filtering in accelerated cardiac MRI.PMC12620177PJT 17829934038PJT 1751312019-06137Cheng HMCurtis ADMertens AJWright GASheagren CDAdve RSKwong RHfalsePredictive signal modeling and multi-rate filtering in accelerated cardiac MRI.<h4>Purpose</h4>True real-time cardiac MRI (CMR), necessary for capturing live cardiac dynamics and imaging irregular cardiac rhythms, remains challenging. In this article, we move toward real-time CMR in multiple reconstruction frameworks via strategies to predict cardiac motion, improve computational efficiency, reduce artifacts, and preserve spatial resolution.<h4>Theory and methods</h4>A published predictive signal model (PMOT) for imaging irregular cardiac dynamics was modified (mPMOT) to enable efficient computation of state-transition matrices for predicting cardiac motion, as training PMOT is computationally expensive. A multi-rate Kalman filter framework was developed to enable computationally efficient reconstructions of high-resolution, large-matrix CMR datasets. Reconstructions2026-01-01T00:00:00Z2026 Jan2026-06-05T15:19:27.012Z2026-06-05T03:06:18.87ZS-EPMC126201774088387810.1002/mrm.70058