Fitting IVIM with Variable Projection and Simplicial Optimization
This work addresses a domain-specific problem in medical imaging for researchers and clinicians by providing an incremental improvement over existing methods with enhanced fitting procedures.
The authors tackled the challenge of fitting multi-exponential models to Diffusion MRI data by introducing a novel and robust fitting framework for the IVIM microstructural model, which automatically estimates diffusion and perfusion parameters and shows improved disentanglement of model parameters in a reduced subspace.
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.