White Matter Fiber Characterization
A new, versatile optimization technique enables microstructure imaging of crossing white matter fibers. Microstructure Imaging of Crossing (MIX) White Matter Fibers is a novel regression method that can characterize tissue microstructure in the brain white matter from diffusion MRI (dMRI) data. The technique is robust, versatile and utilizes the Variable Separation Method (VSM) to fit existing biophysical models with improved accuracy. A MATLAB implementation of MIX demonstrates its applicability to general microstructure models in fiber crossings using synthetic as well as ex-vivo and in-vivo brain data.
Solves Complex Biophysical Model Fitting Problems
Methods to identify features in complex white matter fiber configurations (e.g. crossings) have largely been overlooked by the dMRI community. Existing methods are limited to models assuming single fiber orientation, relevant to limited brain areas like the corpus callosum, or multiple orientations but without the ability to extract detailed microstructural features. The MIX regression offers the ability to solve complex biophysical model fitting problems and to propose and test new models previously impossible to solve.
BENEFITS AND FEATURES:
- Enables microstructure imaging of crossing white matter fibers
- Based on diffusion MRI (dMRI) data
- Robust and versatile
- Solve complex biophysical model fitting problems
- Complex biophysical model fitting problems
- Testing of new models
Phase of Development Prototyped in MATLAB