Brain Hemodynamic Predictions from Noninvasive Transcranial Doppler Ultrasound and Angiography Data Using Physics-Informed Neural Networks

Determining brain hemodynamics plays a critical role in the diagnosis and treatment of various cerebrovascular diseases. Therefore, there is a need to provide rapid, reliable, physiologically correct, and spatiotemporally resolved hemodynamic data for intracranial arteries. In this work, we put forth a deep learning framework that augments sparse clinical measurements with computational fluid dynamic (CFD) simulations to generate physically consistent hemodynamic parameters and vessel cross-sectional areas in the entire brain vasculature. Transcranial Doppler (TCD) ultrasound is one of the most common techniques in the current clinical workflow that enables the noninvasive measurement of blood flow velocity within the cerebral arteries. Our deep learning framework employs in vivo real-time TCD ultrasound velocity measurements at several spatial positions in the brain and the baseline vessel cross-sectional areas acquired from 3D magnetic resonance imaging (MRI) angiograms to provide high-resolution maps of velocity, area, and pressure in the entire vasculature. We validated the predictions of deep learning models against in vivo velocity measurements from the same subject obtained via 4D flow MRI scans. The key finding here is that the combined effects of uncertainties in outlet boundary condition subscription and physics deficiencies render the conventional purely physics-based computational models unsuccessful in recovering accurate brain hemodynamics. Nonetheless, fusing these models with in vivo clinical measurement through a data-driven approach ameliorates predictions of brain hemodynamic variables substantially. Finally, we showcase the clinical significance of the proposed technique in diagnosing the cerebral vasospasm (CVS) induced by intracranial aneurysm (IA) rupture by approximating the vasospastic vessel’s local diameters.