{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Ismail TF"],"funding":["Deutsche Forschungsgemeinschaft","Horizon 2020 Framework Programme"],"pagination":["826283"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8927633"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["9"],"pubmed_abstract":["Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality, causing over 17. 9 million deaths worldwide per year with associated costs of over $800 billion. Improving prevention, diagnosis, and treatment of CVD is therefore a global priority. Cardiovascular magnetic resonance (CMR) has emerged as a clinically important technique for the assessment of cardiovascular anatomy, function, perfusion, and viability. However, diversity and complexity of imaging, reconstruction and analysis methods pose some limitations to the widespread use of CMR. Especially in view of recent developments in the field of machine learning that provide novel solutions to address existing problems, it is necessary to bridge the gap between the clinical and scientific communities. This review"],"journal":["Frontiers in cardiovascular medicine"],"pubmed_title":["Cardiac MR: From Theory to Practice."],"pmcid":["PMC8927633"],"funding_grant_id":["390727645","867450","390900677"],"pubmed_authors":["Ismail TF","Hammernik K","Weingartner S","Kustner T","Strugnell W","Coletti C","Bozic-Iven M","Correia T"],"additional_accession":[]},"is_claimable":false,"name":"Cardiac MR: From Theory to Practice.","description":"Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality, causing over 17. 9 million deaths worldwide per year with associated costs of over $800 billion. Improving prevention, diagnosis, and treatment of CVD is therefore a global priority. Cardiovascular magnetic resonance (CMR) has emerged as a clinically important technique for the assessment of cardiovascular anatomy, function, perfusion, and viability. However, diversity and complexity of imaging, reconstruction and analysis methods pose some limitations to the widespread use of CMR. Especially in view of recent developments in the field of machine learning that provide novel solutions to address existing problems, it is necessary to bridge the gap between the clinical and scientific communities. This review","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-19T12:53:21.531Z","creation":"2025-04-19T12:53:21.531Z"},"accession":"S-EPMC8927633","cross_references":{"pubmed":["35310962"],"doi":["10.3389/fcvm.2022.826283"]}}