Physics-informed neural networks for improving cerebral hemodynamics
predictions
- URL: http://arxiv.org/abs/2108.11498v1
- Date: Wed, 25 Aug 2021 22:19:41 GMT
- Title: Physics-informed neural networks for improving cerebral hemodynamics
predictions
- Authors: Mohammad Sarabian, Hessam Babaee, Kaveh Laksari
- Abstract summary: In this work, we put forth a physics-informed deep learning framework that augments sparse clinical measurements with fast computational fluid dynamics simulations.
Our framework employs in-vivo real-time TCD velocity measurements at several locations in the brain and the baseline vessel cross-sectional areas acquired from 3D images.
We validated the predictions of our model against in-vivo velocity measurements obtained via 4D MRI scans.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 fast computational fluid dynamics (CFD) simulations to
generate physically consistent and high spatiotemporal resolution of brain
hemodynamic parameters. 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 employs
in-vivo real-time TCD velocity measurements at several locations in the brain
and the baseline vessel cross-sectional areas acquired from 3D angiography
images, and provides high-resolution maps of velocity, area, and pressure in
the entire vasculature. We validated the predictions of our model against
in-vivo velocity measurements obtained via 4D flow MRI scans. We then showcased
the clinical significance of this technique in diagnosing the cerebral
vasospasm (CVS) by successfully predicting the changes in vasospastic local
vessel diameters based on corresponding sparse velocities measurements. The key
finding here is that the combined effects of uncertainties in outlet boundary
condition subscription and modeling physics deficiencies render the
conventional purely physics-based computational models unsuccessful in
recovering accurate brain hemodynamics. Nonetheless, fusing these models with
clinical measurements through a data-driven approach ameliorates predictions of
brain hemodynamic variables.
Related papers
- Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
In this study, we first construct the brain-effective network via the dynamic causal model.
We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE)
This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
arXiv Detail & Related papers (2024-05-21T20:37:07Z) - BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations [67.79256149583108]
We propose a novel model called BrainODE to achieve continuous modeling of dynamic brain signals.
By learning latent initial values and neural ODE functions from irregular time series, BrainODE effectively reconstructs brain signals at any time point.
arXiv Detail & Related papers (2024-04-30T10:53:30Z) - SE(3) symmetry lets graph neural networks learn arterial velocity
estimation from small datasets [3.861633648502351]
Hemodynamic velocity fields in coronary arteries could be the basis of valuable biomarkers for diagnosis, prognosis and treatment planning.
Velocity fields are typically obtained from patient-specific 3D artery models via computational fluid dynamics (CFD)
We propose graph neural networks (GNN) as an efficient black-box surrogate method to estimate 3D velocity fields.
arXiv Detail & Related papers (2023-02-17T09:42:38Z) - Transient Hemodynamics Prediction Using an Efficient Octree-Based Deep
Learning Model [0.0]
We present an architecture that is tailored to predict high-resolution (spatial and temporal) velocity fields for complex synthetic vascular geometries.
Compared to CFD simulations, the velocity field can be estimated with a mean absolute error of 0.024 m/s, whereas the run time reduces from several hours on a high-performance cluster to a few seconds on a consumer graphical processing unit.
arXiv Detail & Related papers (2023-02-13T17:56:00Z) - Mesh Neural Networks for SE(3)-Equivariant Hemodynamics Estimation on the Artery Wall [13.113110989699571]
We consider the estimation of vector-valued quantities on the wall of three-dimensional geometric artery models.
We employ group equivariant graph convolution in an end-to-end SE(3)-equivariant neural network that operates directly on triangular surface meshes.
We show that our method is powerful enough to accurately predict transient, vector-valued WSS over the cardiac cycle while conditioned on a range of different inflow boundary conditions.
arXiv Detail & Related papers (2022-12-09T18:16:06Z) - fMRI from EEG is only Deep Learning away: the use of interpretable DL to
unravel EEG-fMRI relationships [68.8204255655161]
We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data.
We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
arXiv Detail & Related papers (2022-10-23T15:11:37Z) - Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and
Methodologies from CNN, GAN to Attention and Transformers [72.047680167969]
This article aims to introduce the deep learning based data driven techniques for fast MRI including convolutional neural network and generative adversarial network based methods.
We will detail the research in coupling physics and data driven models for MRI acceleration.
Finally, we will demonstrate through a few clinical applications, explain the importance of data harmonisation and explainable models for such fast MRI techniques in multicentre and multi-scanner studies.
arXiv Detail & Related papers (2022-04-01T22:48:08Z) - Machine-Learning Identification of Hemodynamics in Coronary Arteries in
the Presence of Stenosis [0.0]
An artificial neural network (ANN) model is trained using synthetic data to predict the pressure and velocity within the arterial network.
The efficiency of the model was verified using three real geometries of LAD's vessels.
arXiv Detail & Related papers (2021-11-02T23:51:06Z) - Simultaneous boundary shape estimation and velocity field de-noising in
Magnetic Resonance Velocimetry using Physics-informed Neural Networks [70.7321040534471]
Magnetic resonance velocimetry (MRV) is a non-invasive technique widely used in medicine and engineering to measure the velocity field of a fluid.
Previous studies have required the shape of the boundary (for example, a blood vessel) to be known a priori.
We present a physics-informed neural network that instead uses the noisy MRV data alone to infer the most likely boundary shape and de-noised velocity field.
arXiv Detail & Related papers (2021-07-16T12:56:09Z) - Microvascular Dynamics from 4D Microscopy Using Temporal Segmentation [81.30750944868142]
We are able to track changes in cerebral blood volume over time and identify spontaneous arterial dilations that propagate towards the pial surface.
This new imaging capability is a promising step towards characterizing the hemodynamic response function upon which functional magnetic resonance imaging (fMRI) is based.
arXiv Detail & Related papers (2020-01-14T22:55:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.