dMRI-IVIM-ML-Toolkit

dMRI-IVIM-ML-Toolkit: Machine Learning for Diffusion MRI Analysis

This repository hosts the source code and implementation details for the paper:

"Exploring the Potential of Machine Learning Algorithms to Improve Diffusion Nuclear Magnetic Resonance Imaging Models Analysis"
Prieto-González LS, Agulles-Pedrós L.
Journal of Medical Physics, 2024.

Read the full paper here

Project Overview

Diffusion MRI analysis, particularly Intravoxel Incoherent Motion (IVIM) and Diffusion Kurtosis Imaging (DKI), often relies on non-linear least squares fitting. While standard, this approach can be computationally expensive and sensitive to noise, especially for parameters like pseudo-diffusion ($D^*$) and perfusion fraction ($f$).

This project introduces a Python-based Machine Learning framework (utilizing Random Forest, Extra Trees, and MLP) to estimate these parameters directly from the signal attenuation curve. It serves as a comprehensive toolkit for researchers looking to implement IVIM Machine Learning workflows in their Python pipelines.

Key Findings

• Computational Efficiency: The ML approach reduces processing time significantly—from approximately 75 minutes to under 20 seconds for 100,000 voxels (a ~230x speedup).
• Noise Robustness: The algorithms demonstrate improved stability in parameter estimation across varying SNR levels compared to conventional fitting.
• Versatility: Validated across different anatomical regions (Prostate, Brain, Head & Neck) and species.

Figure: Visual comparison of parameter maps derived from standard fitting versus the proposed ML estimation.

Installation

To set up the environment, clone this repository and install the required dependencies:

git clone https://github.com/lsprietog/public_release.git
cd public_release
pip install -r requirements.txt

Usage Example

We provide a demonstration script demo.py that generates synthetic IVIM-DKI data to train and validate the model.

To run the demo locally:

python demo.py

Integration with NIfTI Data

The IVIMRegressor class is designed to work with standard numpy arrays derived from NIfTI files.

from src.ml_models import IVIMRegressor
import nibabel as nib
import numpy as np

# Example workflow
# 1. Prepare your data: Shape should be (n_voxels, n_b_values)
# X_train, y_train = load_your_data(...)

# 2. Initialize the regressor (e.g., Extra Trees)
model = IVIMRegressor(model_type='extra_trees')

# 3. Train the model
model.train(X_train, y_train)

# 4. Predict on new data
# X_new shape: (n_voxels_in_volume, n_b_values)
estimated_parameters = model.predict(X_new)

Citation

If you find this repository useful for your research, please consider citing our work:

@article{PrietoGonzalez2024,
  title={Exploring the Potential of Machine Learning Algorithms to Improve Diffusion Nuclear Magnetic Resonance Imaging Models Analysis},
  author={Prieto-González, Leonar Steven and Agulles-Pedrós, Luis},
  journal={Journal of Medical Physics},
  volume={49},
  issue={2},
  pages={189--202},
  year={2024},
  publisher={Wolters Kluwer - Medknow},
  doi={10.4103/jmp.jmp_10_24},
  url={https://pmc.ncbi.nlm.nih.gov/articles/PMC11309135/}
}

Contact

For inquiries regarding the code or the paper, please open an issue in this repository.