Model-based DTI Reconstruction with Variational Constraints

A method for parameter mapping in accelerated MRI.

We are sharing MATLAB code for model-based diffusion tensor imaging (TDI) reconstruction with variational constraints on the tensor elements.

Dotted spheres illustrate diffusion measurements obtained with different techniques in a research phantom with known diffusion properties.

Clockwise from top left: gridding (Grid), parallel imaging combined with compressed sensing and diffusion weighted imaging (PI-CS DWI), model-based diffusion tensor imaging (Model DTI), and model-based diffusion weighted imaging (Model DWI).

Color-coded dots mark data acquired in three planes: axial (red), coronal (green) and oblique (blue).

Wider dot dispersion indicates greater deviation from ideal measurement. Conversely, narrower dot concentration indicates more accurate reconstruction.

The method uses redundancies present in diffusion-weighted image data to reduce the number of unknowns in the optimization problem. A total-variation constrain imposed on the elements of the diffusion tensor enables compressed sensing to be performed directly in the target quantitative domain.

For more information, see the related publication.

Related Publication

Knoll F, Raya JG, Halloran RO, Baete S, Sigmund E, Bammer R, Block T, Otazo R, Sodickson DK.
A model-based reconstruction for undersampled radial spin-echo DTI with variational penalties on the diffusion tensor.
NMR Biomed. 2015 Mar;28(3):353-66. doi: 10.1002/nbm.3258

Please cite this work if you are using model-based DTI reconstruction with variational constraints in your research.

Get the Code

The software available on this page is provided free of charge and comes without any warranty. CAI²R and NYU Grossman School of Medicine do not take any liability for problems or damage of any kind resulting from the use of the files provided. Operation of the software is solely at the user’s own risk. The software developments provided are not medical products and must not be used for making diagnostic decisions.

The software is provided for non-commercial, academic use only. Usage or distribution of the software for commercial purpose is prohibited. All rights belong to the author (Florian Knoll) and NYU Grossman School of Medicine. If you use the software for academic work, please give credit to the author in publications and cite the related publications.

Please spell out your affiliation (e.g. "New York University" rather than "NYU").


Questions about this resource may be directed to Florian Knoll, PhD.