Deep learning-based surrogate model for 3-D patient-specific computational fluid dynamics

Abstract

Optimization and uncertainty quantification have been playing an increasingly important role in computational hemodynamics. However, existing methods based on principled modeling and classic numerical techniques have faced significant challenges, particularly when it comes to complex 3D patient-specific shapes in the real world. First, it is notoriously challenging to parameterize the input space of arbitrarily complex 3-D geometries. Second, the process often involves massive forward simulations, which are extremely computationally demanding or even infeasible. We propose a novel deep learning surrogate modeling solution to address these challenges and enable rapid hemodynamic predictions. Specifically, a statistical generative model for 3-D patient-specific shapes is developed based on a small set of baseline patient-specific geometries. An unsupervised shape correspondence solution is used to enable geometric morphing and scalable shape synthesis statistically. Moreover, a simulation routine is developed for automatic data generation by automatic meshing, boundary setting, simulation, and post-processing. An efficient supervised learning solution is proposed to map the geometric inputs to the hemodynamics predictions in latent spaces. Numerical studies on aortic flows are conducted to demonstrate the effectiveness and merit of the proposed techniques.

arXiv comment8 figures, 2 tables

References in corpus (10)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
• Tune: A Research Platform for Distributed Model Selection and Training
• Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics
• Automated generation of 0D and 1D reduced-order models of patient-specific blood flow
• A System for Massively Parallel Hyperparameter Tuning
• Overcoming the curse of dimensionality in the numerical approximation of semilinear parabolic partial differential equations
• Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning
• A Bi-fidelity Surrogate Modeling Approach for Uncertainty Propagation in Three-Dimensional Hemodynamic Simulations
• A Bi-fidelity Ensemble Kalman Method for PDE-Constrained Inverse Problems

Cited by in corpus (4)

• DeepFEA: Deep Learning for Prediction of Transient Finite Element Analysis Solutions
• Stochastic Modeling of Inhomogeneities in the Aortic Wall and Uncertainty Quantification using a Bayesian Encoder-Decoder Surrogate
• The Anatomy of Coronary Risk: How Artery Geometry Shapes Coronary Artery Disease through Blood Flow Haemodynamics -- Latest Methods, Insights and Clinical Implications
• Residual U-Net for accurate and efficient prediction of hemodynamics in two-dimensional asymmetric stenosis