Microstructure Imaging of Crossing White Matter Fibers from Diffusion MRI

Technology No. 20170140

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.

The software is publically available under the GPL v3.0 license and can be downloaded from the CMRR MIX webpage. (For an alternative license, Please contact us).

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.


  • Enables microstructure imaging of crossing white matter fibers
  • Based on diffusion MRI (dMRI) data
  • Robust and versatile
  • Solve complex biophysical model fitting problems


  • MRI
  • Complex biophysical model fitting problems
  • Testing of new models

Phase of Development Prototyped in MATLAB

Christophe Lenglet
Assistant Professor, Department of Radiology, Center for Magnetic Resonance Research
External Link (www.radiology.umn.edu)
Tryphon Georgiou
Professor Emeritus, CSENG Electrical and Computer Engineering
External Link (ece.umn.edu)
Hamza Farooq
PhD Research Assistant, Electrical and Computer Engineering

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