Title
Emerging Techniques in Cardiac Magnetic Resonance Imaging
Author
Guo, Rui (Harvard Medical School; Beth Israel Deaconess Medical Center)
Weingärtner, S.D. (TU Delft ImPhys/Computational Imaging; TU Delft ImPhys/Medical Imaging) 
Šiuryté, P. (TU Delft ImPhys/Medical Imaging)
T. Stoeck, Christian (ETH Zürich)
Füetterer, Maximilian (ETH Zürich)
E. Campbell-Washburn, Adrienne (National Heart, Lung, and Blood Institute)
Suinesiaputra, Avan (University of Leeds)
Jerosch-Herold, Michael (Brigham and Women's Hospital; Harvard Medical School)
Nezafat, Reza (Harvard Medical School; Beth Israel Deaconess Medical Center)
Date
2021
Abstract
Cardiovascular disease is the leading cause of death and a significant contributor of health care costs. Noninvasive imaging plays an essential role in the management of patients with cardiovascular disease. Cardiac magnetic resonance (MR) can noninvasively assess heart and vascular abnormalities, including biventricular structure/function, blood hemodynamics, myocardial tissue composition, microstructure, perfusion, metabolism, coronary microvascular function, and aortic distensibility/stiffness. Its ability to characterize myocardial tissue composition is unique among alternative imaging modalities in cardiovascular disease. Significant growth in cardiac MR utilization, particularly in Europe in the last decade, has laid the necessary clinical groundwork to position cardiac MR as an important imaging modality in the workup of patients with cardiovascular disease. Although lack of availability, limited training, physician hesitation, and reimbursement issues have hampered widespread clinical adoption of cardiac MR in the United States, growing clinical evidence will ultimately overcome these challenges. Advances in cardiac MR techniques, particularly faster image acquisition, quantitative myocardial tissue characterization, and image analysis have been critical to its growth. In this review article, we discuss recent advances in established and emerging cardiac MR techniques that are expected to strengthen its capability in managing patients with cardiovascular disease. Level of Evidence: 5. Technical Efficacy: Stage 1.
Subject
cardiac magnetic resonance
deep learning
low-field imaging
machine learning
myocardial tissue characterization
radiomics
To reference this document use:
http://resolver.tudelft.nl/uuid:0d73fd0b-e323-4f37-a1a8-73acc46afb6d
DOI
https://doi.org/10.1002/jmri.27848
Embargo date
2022-07-31
ISSN
1053-1807
Source
Journal of Magnetic Resonance Imaging, 55 (4), 1043-1059
Bibliographical note
Accepted Author Manuscript
Part of collection
Institutional Repository
Document type
review
Rights
© 2021 Rui Guo, S.D. Weingärtner, P. Šiuryté, Christian T. Stoeck, Maximilian Füetterer, Adrienne E. Campbell-Washburn, Avan Suinesiaputra, Michael Jerosch-Herold, Reza Nezafat