SCH: A physics-informed machine learning approach to dynamic blood flow analysis from static subtraction computed tomographic angiography imaging

University Of Wisconsin-Milwaukee, Milwaukee WI

Investigators

Abstract

Recent investigations have shown that interactions of blood flow with blood vessel walls plays an important role in the progression of cardiovascular diseases. Accurately quantifying blood flow or hemodynamic interactions could lead to methods for patient-specific therapies that result in better treatments and reduced mortality. In this project, the researchers will develop techniques to non-invasively inferring the complex, dynamic hemodynamic behavior using a commonly used medical imaging modality that is typically used to produce static anatomical images for analyzing blood vessel structure. In this project, the researchers propose to develop a novel physics-informed model of the blood flow using a deep-learning based processing method. This will allow the researchers infer dynamic time-resolved three-dimensional blood velocity and relative pressure field. The results will be used to accurately compute relevant hemodynamic factors. This project will train a cohort of graduate students in the latest data-driven deep learning techniques in engineering. It will engage undergraduate students in research through well-established programs at UW Milwaukee and Northern Arizona University. Outreach to high school students, particularly those belonging to under-represented communities will be accomplished through summer programs at UW Milwaukee. The goal of this project is accurate image-based hemodynamic analysis using commonly available images. Contrast concentration, three-dimensional blood velocity, and relative pressure will be modeled as deep neural nets. Training the neural nets will involve a loss function that matches actual data from time-stamped sCTA sinograms with predicted sinograms generated using line integrals computed from forward evaluation of the neural net used to model the contrast concentration. Additionally, blood flow and contrast advection-diffusion physics will be used as constraints in the solution process. System noise will be handled through a Bayesian formulation of the deep learning algorithm. The neural net formulation will allow high resolution sampling of the blood velocity and relative pressure fields and accurate computation of velocity-derive hemodynamic parameters using automatic differentiation. The methods will be validated using numerical and in vitro flow experiments using particle image velocimetry. By enabling the estimation of hemodynamic data from what, until now, has been considered to be static data, the proposed research maximizes inference that can be derived from sCTA imaging data without the need for additional computed tomography hardware or new scan protocols. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.