Super-resolution left ventricular flow and pressure mapping by Navier-Stokes-informed neural networks

Bahetihazi Maidu; Pablo Martinez-Legazpi; Manuel Guerrero-Hurtado; Cathleen M Nguyen; Alejandro Gonzalo; Andrew M Kahn; Javier Bermejo; Oscar Flores; Juan C Del Alamo

doi:10.1016/j.compbiomed.2024.109476

Super-resolution left ventricular flow and pressure mapping by Navier-Stokes-informed neural networks

Comput Biol Med. 2024 Dec 12:185:109476. doi: 10.1016/j.compbiomed.2024.109476. Online ahead of print.

Authors

Bahetihazi Maidu¹, Pablo Martinez-Legazpi², Manuel Guerrero-Hurtado³, Cathleen M Nguyen¹, Alejandro Gonzalo¹, Andrew M Kahn⁴, Javier Bermejo⁵, Oscar Flores³, Juan C Del Alamo⁶

Affiliations

¹ Dept. of Mechanical Engineering, University of Washington, Seattle, WA, USA.
² Dept. of Mathematical Physics and Fluids. Universidad Nacional de Educación a Distancia & CIBERCV, Madrid, Spain.
³ Dept. of Aerospace Engineering, Universidad Carlos III de Madrid, Leganes, Spain.
⁴ Dept. of Cardiology, Hospital General Universitario Gregorio Marañon & CIBERCV, Madrid, Spain.
⁵ Division of Cardiovascular Medicine., University of California San Diego, La Jolla, CA, USA.
⁶ Dept. of Mechanical Engineering, University of Washington, Seattle, WA, USA; Center for Cardiovascular Biology, University of Washington School of Medicine, Seattle, WA, USA; Division of Cardiology, University of Washington School of Medicine, Seattle, WA, USA. Electronic address: juancar@uw.edu.

PMID: 39672010
DOI: 10.1016/j.compbiomed.2024.109476

Abstract

Intraventricular vector flow mapping (VFM) is an increasingly adopted echocardiographic technique that derives time-resolved two-dimensional flow maps in the left ventricle (LV) from color-Doppler sequences. Current VFM models rely on kinematic constraints arising from planar flow incompressibility. However, these models are not informed by crucial information about flow physics; most notably the forces within the fluid and the resulting accelerations. This limitation has rendered VFM unable to combine information from different time frames in an acquisition sequence or derive fluctuating pressure maps. In this study, we leveraged recent advances in artificial intelligence (AI) to develop AI-VFM, a vector flow mapping modality that uses physics-informed neural networks (PINNs) encoding mass conservation and momentum balance inside the LV, and no-slip boundary conditions at the LV endocardium. AI-VFM recovers the flow and pressure fields in the LV from standard echocardiographic scans. It performs phase unwrapping and recovers flow data in areas without input color-Doppler data. AI-VFM also recovers complete flow maps at time points without color-Doppler input data, producing super-resolution flow maps. We validate AI-VFM using physiological simulated LV data and show that informing the PINNs with momentum balance is essential for achieving temporal super-resolution and significantly increases the accuracy of AI-VFM compared to informing the PINNs only with mass conservation. AI-VFM is solely informed by each patient's flow physics; it does not utilize explicit smoothness constraints or incorporate data from other patients or flow models. AI-VFM takes 15 min to run in off-the-shelf graphics processing units and its underlying PINN framework could be extended to map other flow-associated metrics such as blood residence time or the concentration of coagulation species.

Keywords: Cardiovascular flow; Deep learning; Echocardiography; Physics-informed neural networks; Super-resolution flow imaging; Vector flow imaging.