We are sharing a robust implementation of parameter estimation for the standard model of diffusion in white matter along with example datasets. The toolbox can be used to analyze diffusion MRI images and extract information about various structures present in a voxel of white matter, such as axons, extra-axonal space, and cerebrospinal fluid.
The standard model is a unifying framework of white matter diffusion models, most of which are described by the same underlying physics (NMR Biomed. 2019;32:e3998. doi: 10.1002/nbm.3998). In such a framework, most diffusion models can be understood as special cases of the standard model (Neuroimage. 2018;174:518-538. doi: 10.1016/j.neuroimage.2018.03.006).
The SMI Toolbox supports:
- Linear tensor encoding data only (i.e. single diffusion encoding) and equal echo time (TE) in all diffusion-weighted images.
- Multiple tensor encodings and equal TE in all diffusion-weighted images.
- Multiple tensor encodings and differing TE between all diffusion-weighted images (when multiple TE data are used, the SMI Toolbox returns axonal and extra-axonal T2 estimates).
For best results, we suggest preprocessing the diffusion-weighted data with the DESIGNER pipeline, which outputs a noise map that can be used as an input for the SMI Toolbox.
Reproducibility of the Standard Model of diffusion in white matter on clinical MRI systems.
Neuroimage. 2022 May 8:119290. doi: 10.1016/j.neuroimage.2022.119290
Please cite this work if you are using the SMI Toolbox in your research.
Rotationally-invariant mapping of scalar and orientational metrics of neuronal microstructure with diffusion MRI.
Neuroimage. 2018;174:518-538. doi: 10.1016/j.neuroimage.2018.03.006
Disentangling micro from mesostructure by diffusion MRI: A Bayesian approach.
Neuroimage. 2017;147:964-975. doi: 10.1016/j.neuroimage.2016.09.058
System, method and computer-accessible medium for determining brain microstructure parameters from diffusion magnetic resonance imaging signal’s rotational invariants.
US Patent 2016/0343129 A1. July 23, 2019.
Get the Code
This resource is maintained on Github as Standard Model Imaging (SMI) Toolbox.
Below, we provide three example datasets for easy testing of the SMI toolbox. All data were preprocessed with DESIGNER.
dataset_1contains a 2-shell diffusion MRI protocol similar to the one in the UK biobank.
dataset_2contains multiple b-value b-tensor shape combinations (all with the same echo time), similar to the one we proposed in the related publication cited above.
dataset_3contains multiple combinations of b-values, b-tensor shapes, and echo times.
Note that all three datasets are provided in nifti format. We used Jimmy Shen’s Tools for NIfTI and ANALYZE image toolbox to handle the data in MATLAB but users may turn to other tools of their choice and modify the lines in the example code.
Get the Data
The data available on this page are 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 (Santiago Coelho) 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.
Questions about this resource may be directed to Santiago Coelho, PhD, or raised as issues on Github.