This repository contains the code regarding our submitted publication: Physics-informed neural networks improve three-component model fitting of intravoxel incoherent motion MR imaging in cerebrovascular disease

This code synthesizes three-component IVIM decay curves. Several fitting algorithms are implemented to fit the IVIM model to these synthesized curves: least squares (LSQ), non-negative least squares (NNLS) and physics-informed neural networks (PI-NN). Several options for the PI-NN architecture and IVIM parameter constraints can be explored.

To directly run the code, we added a '.yml' file which can be run in anaconda. To create a conda environment with the '.yml' file enter the command below in the terminal: conda env create -f environment.yml

This now creates an environment called 'ivim' that can be activated by: conda activate ivim

Run Example_simulations.py to perform the simulations as described in our publication

joblib=0.17.0 matplotlib=3.3.2 nibabel=3.2.0 numpy=1.19.2 python=3.6.12 pytorch=1.4.0 scipy=1.5 tqdm=4.50.2

Paulien Voorter [email protected] | [email protected] | https://github.com/paulienvoorter

January 2022, Paulien Voorter

June 2021, Oliver Gurney-Champion and Misha Kaandorp https://github.com/oliverchampion/IVIMNET

August 2019, Sebastiano Barbieri: https://github.com/sebbarb/deep_ivim