4.7 Article

Physics-Informed Neural Networks for Brain Hemodynamic Predictions Using Medical Imaging

期刊

IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 41, 期 9, 页码 2285-2303

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2022.3161653

关键词

Magnetic resonance imaging; Hemodynamics; Computational modeling; Brain modeling; Blood; Velocity measurement; Arteries; Deep neural networks; brain hemodynamics; transcranial Doppler ultrasound; 4D flow MRI

资金

  1. National Institutes of Health (NIH) National Institute of Neurological Disorders and Stroke (NINDS) [R03NS108167]
  2. National Institute of Biomedical Imaging and Bioengineering (NIBIB) Trailblazer award [R21EB032187]
  3. University of Arizona's 18th Mile TRIF Fund

向作者/读者索取更多资源

This study proposes a physics-informed deep learning framework that utilizes a reduced-order model simulation to generate high-resolution brain hemodynamic parameters, augmenting sparse clinical measurements. The framework is validated against in vivo velocity measurements obtained from MRI scans and shows potential in diagnosing cerebral vasospasm.
Determining brain hemodynamics plays a critical role in the diagnosis and treatment of various cerebrovascular diseases. In this work, we put forth a physics-informed deep learning framework that augments sparse clinical measurements with one-dimensional (1D) reduced-order model (ROM) simulations to generate physically consistent brain hemodynamic parameters with high spatiotemporal resolution. Transcranial Doppler (TCD) ultrasound is one of the most common techniques in the current clinical workflow that enables noninvasive and instantaneous evaluation of blood flow velocity within the cerebral arteries. However, it is spatially limited to only a handful of locations across the cerebrovasculature due to the constrained accessibility through the skull's acoustic windows. Our deep learning framework uses in vivo real-time TCD velocity measurements at several locations in the brain combined with baseline vessel cross-sectional areas acquired from 3D angiography images and provides high-resolution maps of velocity, area, and pressure in the entire brain vasculature. We validate the predictions of our model against in vivo velocity measurements obtained via four-dimensional (4D) flow magnetic resonance imaging (MRI) scans. We then showcase the clinical significance of this technique in diagnosing cerebral vasospasm (CVS) by successfully predicting the changes in vasospastic local vessel diameters based on corresponding sparse velocity measurements. We show this capability by generating synthetic blood flow data after cerebral vasospasm at various levels of stenosis. Here, we demonstrate that the physics-based deep learning approach can estimate and quantify the subject-specific cerebral hemodynamic variables with high accuracy despite lacking knowledge of inlet and outlet boundary conditions, which is a significant limitation for the accuracy of the conventional purely physics-based computational models.

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