{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Tuna EE"],"funding":["Directorate for Engineering","National Heart, Lung, and Blood Institute","NHLBI NIH HHS","Directorate for Computer and Information Science and Engineering"],"pagination":["1387"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9877032"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13(1)"],"pubmed_abstract":["This study presents a particle filter based framework to track cardiac surface from a time sequence of single magnetic resonance imaging (MRI) slices with the future goal of utilizing the presented framework for interventional cardiovascular magnetic resonance procedures, which rely on the accurate and online tracking of the cardiac surface from MRI data. The framework exploits a low-order parametric deformable model of the cardiac surface. A stochastic dynamic system represents the cardiac surface motion. Deformable models are employed to introduce shape prior to control the degree of the deformations. Adaptive filters are used to model complex cardiac motion in the dynamic model of the system. Particle filters are utilized to recursively estimate the current state of the system over time"],"journal":["Scientific reports"],"pubmed_title":["Deformable cardiac surface tracking by adaptive estimation algorithms."],"pmcid":["PMC9877032"],"funding_grant_id":["R01 HL153034","IIS-1563805","IIS-1524363","IIP-1700839"],"pubmed_authors":["Tuna EE","Cavusoglu MC","Franson D","Seiberlich N"],"additional_accession":[]},"is_claimable":false,"name":"Deformable cardiac surface tracking by adaptive estimation algorithms.","description":"This study presents a particle filter based framework to track cardiac surface from a time sequence of single magnetic resonance imaging (MRI) slices with the future goal of utilizing the presented framework for interventional cardiovascular magnetic resonance procedures, which rely on the accurate and online tracking of the cardiac surface from MRI data. The framework exploits a low-order parametric deformable model of the cardiac surface. A stochastic dynamic system represents the cardiac surface motion. Deformable models are employed to introduce shape prior to control the degree of the deformations. Adaptive filters are used to model complex cardiac motion in the dynamic model of the system. Particle filters are utilized to recursively estimate the current state of the system over time","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Jan","modification":"2025-04-26T01:44:57.628Z","creation":"2025-04-06T10:12:02.841Z"},"accession":"S-EPMC9877032","cross_references":{"pubmed":["36697497"],"doi":["10.1038/s41598-023-28578-0"]}}