Optical Coherence Tomography (OCT) offers an exciting opportunity to probe both the structural and functional properties of tissue. In particular, intravascular OCT (IV-OCT) has emerged as a promising tool for investigating the vasculature for concerns such as stenosis and calcification while also identifying success of stent deployment. Prior work has established physics-based inverse rendering models that characterize fibrous caps, lipid pools, calcifications, and healthy vessel walls by resolving the attenuation signatures that distinguish these tissue types. Yet these models return point estimates without any measure of confidence, so measurements drawn from shadowed or deep-tissue regions can appear just as trustworthy as well-resolved ones. This project centers on uncertainty quantification to close that gap: distinguishing which measurements are reliable and which break down under poor signal conditions. Beyond broadening our understanding of how optical technologies extract pathological information from phenomena governed by the Beer-Lambert law, this work establishes a more general methodology for uncertainty-aware differential rendering across modalities and applications.

Through this SuperUROP, I am looking forward to enhancing the fidelity of inverse rendering models in the medical imaging landscape. I am drawn to quantitative physiology, and even more so to how optical techniques can guide clinical decision-making by deriving pathological insight that would otherwise be inaccessible. Long term, I am passionate about using computational tools to improve diagnostics and treatment monitoring in cardiology, so this project is a concrete first step toward the kind of diagnostic tools I want to spend my career building.