Fitting IVIM with Variable Projection and Simplicial Optimization

TL;DR

This paper introduces a new robust framework for fitting the IVIM model in diffusion MRI, improving parameter estimation accuracy and automation through advanced optimization techniques, and providing open-source tools for the community.

Contribution

The work presents a novel fitting framework for IVIM that integrates variable projection and simplicial optimization, enhancing robustness and automation over previous methods.

Findings

Improved parameter disentanglement in IVIM fitting.

Enhanced robustness with advanced global optimization methods.

Open-source implementation available for community use.

Abstract

Fitting multi-exponential models to Diffusion MRI (dMRI) data has always been challenging due to various underlying complexities. In this work, we introduce a novel and robust fitting framework for the standard two-compartment IVIM microstructural model. This framework provides a significant improvement over the existing methods and helps estimate the associated diffusion and perfusion parameters of IVIM in an automatic manner. As a part of this work we provide capabilities to switch between more advanced global optimization methods such as simplicial homology (SH) and differential evolution (DE). Our experiments show that the results obtained from this simultaneous fitting procedure disentangle the model parameters in a reduced subspace. The proposed framework extends the seminal work originated in the MIX framework, with improved procedures for multi-stage fitting. This framework has been made available as an open-source Python implementation and disseminated to the community through the DIPY project.