Cardiovascular Magnetic Resonance: Deeper Insights Through Bioengineering

Author:

Young A.A.1,Prince J.L.2

Affiliation:

1. Department of Anatomy with Radiology, School of Medical Science, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand;

2. Department of Electrical and Computer Engineering, Center for Imaging Science, John Hopkins University, Baltimore, Maryland 21218

Abstract

Heart disease is the main cause of morbidity and mortality worldwide, with coronary artery disease, diabetes, and obesity being major contributing factors. Cardiovascular magnetic resonance (CMR) can provide a wealth of quantitative information on the performance of the heart, without risk to the patient. Quantitative analyses of these data can substantially augment the diagnostic quality of CMR examinations and can lead to more effective characterization of disease and quantification of treatment benefit. This review provides an overview of the current state of the art in CMR with particular regard to the quantification of motion, both microscopic and macroscopic, and the application of bioengineering analysis for the evaluation of cardiac mechanics. We discuss the current clinical practice and the likely advances in the next 5–10 years, as well as the ways in which clinical examinations can be augmented by bioengineering analysis of strain, compliance, and stress.

Publisher

Annual Reviews

Subject

Biomedical Engineering,Medicine (miscellaneous)

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Fully Automated Myocardial Strain Estimation from Cardiovascular MRI–tagged Images Using a Deep Learning Framework in the UK Biobank;Radiology: Cardiothoracic Imaging;2020-02-01

2. Positron Emission Tomography;Cardiac CT, PET & MR;2019-07-05

3. A Meshfree Representation for Cardiac Medical Image Computing;IEEE Journal of Translational Engineering in Health and Medicine;2018

4. Robust recovery of myocardial kinematics using dual ℋ ∞ $\mathcal {H}_{\infty }$ criteria;Multimedia Tools and Applications;2017-11-20

5. Image-Based Predictive Modeling of Heart Mechanics;Annual Review of Biomedical Engineering;2015-12-07

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