Quantitative Coronary Hemodynamics Derived from CT Angiography

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DESCRIPTION (provided by applicant): This mentored career development award will enable the training and career transition of a productive, innovative, successful junior scientist. A key element is training and mentorship by thought-leaders in the field of cardiovascular diagnostic imaging and medicine. The applicant, Dr. Mitsouras, has been trained in computer science, physics, and applied mathematics at Brown and MIT. He is a successful MR physicist with important contributions to the field. He is Assistant Professor of Radiology at Harvard, and the Director of MR Physics and Engineering of the BWH Applied Imaging Science Lab (AISL), directed by Dr. Rybicki. A successful PI and skilled mentor, Dr. Rybicki will enthusiastically support and mentor Dr. Mitsouras to career independence. Noninvasive imaging for patients with coronary artery disease (CAD) is gravitating to CT; the proposed research addresses some of its current limitations in clinical care. The AISL evaluates new CT technologies, and has studied and published the potential of coronary enhancement to extend clinical information. It is recognized that the next leap in clinical cardiac CT is flow quantification; Dr. Mitsouras proposes a transformative new approach to this body of work. For the candidate, the transition from MR basic physics to clinical CT applications creates a large, unmet training need. Classroom based (Harvard/MIT Division of Health Sciences and Technology) and practical imaging based (BWH noninvasive cardiovascular imaging program) training will ideally bridge this gap. This K01 proposal also integrates mentorship from renowned experts who know and are eager to work with Dr. Mitsouras. The team includes Dr. Mel Clouse, an exceptional clinician and mentor, Dr. Xiaochuan Pan, an expert in CT physics, Dr. Tae Bae, an expert in contrast delivery, Dr. Al Lardo, an expert in modeling myocardial perfusion, and Dr. Joao Lima, an expert in advanced clinical applications. This exceptional team will navigate Dr. Mitsouras in a structured research project, capitalizing on their collective expertise and Dr. Mitsouras' existing skills. Together, tey will extend the role of noninvasive CAD imaging by applying innovative methods to extract blood flow from clinical CT angiography. At present, noninvasive access to flow is limited to simulated fluid dynamics using an arterial lumen segmented from clinical images. We advance this field with a novel transition from such CT-based flow, to CT-derived flow: quantitative flow that is derived either directly or indirectly from the spatio-temporal patterns of arterial contrast enhancement observed in vivo. A growing body of literature supports qualitative flow information is embedded in CTA enhancement patterns across an artery. We show these patterns in fact possess smooth space and time variations that are intrinsically linked to flow. By unifying signal processing and pattern recognition with high-performance fluid dynamics modeling, this information will be accessed for the first time to quantify true patient-specific flow and yield lesion-specific hemodynamic metrics such as fractional flow reserve.