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

Journal: Computers in biology and medicine
PMID:

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.

Authors

  • Bahetihazi Maidu
    Dept. of Mechanical Engineering, University of Washington, Seattle, WA, USA.
  • Pablo Martinez-Legazpi
    Dept. of Mathematical Physics and Fluids. Universidad Nacional de Educación a Distancia & CIBERCV, Madrid, Spain.
  • Manuel Guerrero-Hurtado
    Dept. of Aerospace Engineering, Universidad Carlos III de Madrid, Leganes, Spain.
  • Cathleen M Nguyen
    Dept. of Mechanical Engineering, University of Washington, Seattle, WA, USA.
  • Alejandro Gonzalo
    Dept. of Mechanical Engineering, University of Washington, Seattle, WA, USA.
  • Andrew M Kahn
    Human Longevity, Inc., San Diego, CA, 92121, USA.
  • Javier Bermejo
    Department of Cardiology, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Hospital General Universitario Gregorio Marañón, Madrid, Spain.
  • Oscar Flores
    Dept. of Aerospace Engineering, Universidad Carlos III de Madrid, Leganes, Spain.
  • Juan C Del Alamo
    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.