{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Curtis AD"],"funding":["Natural Sciences & Engineering Research Council of Canada (NSERC)","The Dean's Strategic Fund, Univesity of Toronto","Ontario Graduate Scholarship, Government of Ontario","Canada Foundation for Innovation","CIHR"],"pagination":["585-600"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12620177"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["95(1)"],"pubmed_abstract":["<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. Reconstructions"],"journal":["Magnetic resonance in medicine"],"pubmed_title":["Predictive signal modeling and multi-rate filtering in accelerated cardiac MRI."],"pmcid":["PMC12620177"],"funding_grant_id":["PJT 178299","34038","PJT 175131","2019-06137"],"pubmed_authors":["Cheng HM","Curtis AD","Mertens AJ","Wright GA","Sheagren CD","Adve RS","Kwong RH"],"additional_accession":[]},"is_claimable":false,"name":"Predictive signal modeling and multi-rate filtering in accelerated cardiac MRI.","description":"<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. Reconstructions","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Jan","modification":"2026-06-05T15:19:27.012Z","creation":"2026-06-05T03:06:18.87Z"},"accession":"S-EPMC12620177","cross_references":{"pubmed":["40883878"],"doi":["10.1002/mrm.70058"]}}