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MRE-PINN

This repository contains code for the paper Physics-informed neural networks for tissue elasticity reconstruction in magnetic resonance elastography which is to be presented at MICCAI 2023.

MRE-PINN examples

Installation

Run the following to setup the conda environment and register it as a Jupyter notebook kernel:

mamba env create --file=environment.yml
mamba activate MRE-PINN
python -m ipykernel install --user --name=MRE-PINN

Usage

This notebook downloads the BIOQIC simulation data set and trains PINNs to reconstruct a map of shear elasticity from the displacement field.

The notebook takes roughly 2.5 h to train for 100,000 iterations on an RTX 5000 and uses 2.5 GiB of GPU memory.