Noncontrast cardiac MRI for metabolic imaging beyond contraction

Cardiometabolic disease affects over 25% of adults in USA and its prevalence increases with aging population. Considerable disparities in cardiometabolic burden exists across regions, ethnic groups, ages, and comorbidity profile, which underscore the importance for precision diagnosis and treatment. Cardiovascular magnetic resonance (CMR) has emerged as the most comprehensive diagnostic tool for many cardiac diseases including myocardial contraction and scar tissue assessment, yet its sensitivity and specificity to detect cardiac metabolism still need further improvement. In this presentation, I will focus on discussion of our newly developed CMR techniques, enabled with deep learning models, for absolute quantification of myocardial blood flow, oxygenation, and energetics. There is no gadolinium contrast agent involved using these techniques, which is well suited for repeated CMR study and for imaging patients with contraindication to gadolinium contrast agents (renal insufficiency). Although techniques are still in their infancy, some preliminary evaluations in vivo show promising potential for clinical translational applications for better diagnosis of cardiometabolic disease beyond simple contraction assessment by CMR.