Venkatesh L Murthy, MD, PhD

QUANTITATIVE AND AI-ENABLED CARDIOVASCULAR IMAGING
My laboratory develops and validates quantitative methods for cardiac PET, SPECT, CT, and MRI, with particular focus on absolute myocardial blood flow and flow reserve. We apply machine learning and self-supervised foundation models to extract diagnostic and prognostic information from imaging and the electrocardiogram. This work spans motion correction, attenuation correction, tracer kinetic modeling, and the detection of coronary microvascular and vasomotor dysfunction from widely available, inexpensive data.

MOLECULAR EPIDEMIOLOGY OF CARDIOVASCULAR DISEASE
Using proteomics, metabolomics, and RNA profiling integrated with imaging and deep phenotyping in large cohorts (CARDIA, MESA, Health ABC, the Jackson Heart Study, and Indigenous American populations), I study how adiposity, insulin resistance, fitness, and the environmental exposome shape the transition to diabetes, heart failure, and atherosclerotic cardiovascular disease across the lifespan.

CORONARY MICROVASCULAR AND VASOMOTOR DYSFUNCTION
I lead multi-omic and AI-based efforts to define, diagnose, and target coronary microvascular and vasomotor dysfunction, an under-recognized driver of excess cardiovascular risk in women.

METHODS, TRANSLATION, AND CLINICAL IMPACT
Drawing on physics, engineering, biostatistics, and artificial intelligence, my work emphasizes rigorous, clinically deployable tools and their translation into practice and guidelines. This research has been funded as principal investigator by three institutes of the National Institutes of Health (NHLBI, NIDDK, and NIA), by Wellcome Leap, and by foundation and industry partners.