My research focuses on developing advanced medical imaging systems by integrating physics, computer science, deep learning, and medical image analysis. Motivated by personal experiences with sudden cardiac events, I view advancing clinical cardiac interventions as both a scientific and personal mission. My background in physics has enabled a deep understanding of cardiac imaging systems and their underlying principles across diverse imaging modalities.

Through this foundation, I recognized optical imaging as a powerful and safe approach for cardiac diagnosis. In particular, intravascular Optical Coherence Tomography (IV-OCT) provides the highest-resolution intracoronary images currently available. However, despite its advantages, IV-OCT remains underutilized due to the time-consuming and operator-dependent nature of image interpretation. Influenced by insights from Marvin Woodall’s reassessment of coronary stenting outcomes, I recognized that reducing cardiac mortality requires detecting vulnerable plaques—responsible for approximately seventy-five percent of fatal coronary events—rather than focusing solely on stable lesions.

This realization shaped my research direction toward developing accurate, automated methods for intracoronary OCT analysis to identify vulnerable plaque features. I structured my Ph.D. and subsequent academic work around addressing this unmet clinical need, with the goal of enabling operator-independent, clinically scalable tools for improved risk stratification and prevention of sudden cardiac death.

Upon joining ViTAA Medical Solutions as a senior research scientist, I became aware of another crucial clinical need related to abdominal aortic aneurysm (AAA). Because AAA is largely asymptomatic, symptoms typically appear only when the aneurysm has ruptured or is at high risk of rupture. Therefore, assessing disease progression and rupture risk represents a critical clinical challenge.

Although imaging systems are used to evaluate AAA tissue characteristics, computed tomography (CT) imaging is still required to obtain detailed information about disease progression. The wide variability in clinical decision-making and aneurysm repair planning from one patient to another highlights the need for personalized decision-making approaches.

Moreover, the lack of accurate automated techniques for characterizing aortic tissues often necessitates the use of high doses of contrast agents to enable visualization of multiple tissue types. Even then, visual tissue assessment using contrast-enhanced imaging remains time-consuming and prone to interobserver variability.

As a result, my primary focus was to develop a multi-step model, beginning with automated extraction of the aorta and iliac arteries, followed by detection of the lumen and intraluminal thrombus.

Optimal treatment planning for coronary artery disease requires objective evidence of whether a lesion truly disrupts myocardial perfusion, yet clinical decisions are still largely driven by anatomical appearance alone. As a result, intravascular imaging is often applied after lesions have been selected for intervention, increasing unnecessary procedures, procedural risk, and healthcare costs without guaranteeing improved outcomes.

My work addresses this gap by shifting coronary decision-making toward physiology-informed imaging. While IVUS and IV-OCT provide unmatched structural detail, their clinical value is maximized when they are targeted to lesions that demonstrably impair flow or exhibit adverse hemodynamic conditions. Volumetric blood flow quantification and wall shear stress mapping provide this missing physiological context, distinguishing functionally significant lesions from those that are visually severe but hemodynamically benign.

By integrating flow and shear stress into the diagnostic workflow, my research advances a framework in which physiology guides anatomy, enabling intravascular imaging to be used as a precision tool. This vision—linking functional hemodynamics with structural assessment—was the primary motivation for my return to academia in 2024 to pursue flow-based biomarkers for plaque rupture risk prediction and personalized coronary intervention.