Recent advances in cardiac MRI (CMR) capabilities have truly transformed its potential for deep phenotyping of the diseased heart. Long known for its unparalleled soft tissue contrast and excellent depiction of three-dimensional (3D) structure, CMR now boasts a range of unique capabilities for probing disease at the tissue and molecular level. We can look beyond coronary vessel blockages and detect vessel disease not visible on a structural level. We can assess if early fibrotic tissue is being laid down in between viable cardiac muscle cells. We can measure deformation of the heart wall to determine early presentation of stiffening. We can even assess how cardiomyocytes are utilizing energy, where abnormalities are often precursors to overt structural and functional deficits. Finally, with artificial intelligence gaining traction due to the high computing power available today, deep learning has proven itself a viable contender with traditional acceleration techniques for real-time CMR. In this review, we will survey five key emerging MRI techniques that have the potential to transform the CMR clinic and permit early detection and intervention. The emerging areas are: (1) imaging microvascular dysfunction, (2) imaging fibrosis, (3) imaging strain, (4) imaging early metabolic changes, and (5) deep learning for acceleration. Through a concerted effort to develop and translate these areas into the CMR clinic, we are committing ourselves to actualizing early diagnostics for the most intractable heart disease phenotypes.
Keywords: deep learning; fibrosis; heart failure; ischemic disease; metabolism; microvascular dysfunction; non-ischemic disease; strain.
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