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MRIToolkit is a set ofcommand line tools and a MATLAB (R) toolbox to process (diffusion) magnetic resonance imaging (MRI) data. Binaries of the command line version will be provided at some point!

The idea behind MRIToolkit is to integratemy research output with existing state-of-the-art methods for (diffusion) MRI processing.

• The MATLAB (R) toolbox is availablehere on Github!
• The compiledcommand line tools will be uploaded soon!

The toolbox is referenced for the first time inGuo et al.. Additionally, please cite the original works corresponding to the steps you use:

• ExploreDTI: Leemans, A., Jeurissen, B., Sijbers, J., & Jones, D. K. (2009). ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. 17th Annual Meeting of the International Society for Magnetic Resonance in Medicine, Honolulu, Hawaii, USA, 3537.
• Diffusion MRI pre-processing: (signal drift correction,Gibbs ringing correction, motion and eddy currents correction,B-matrix rotation, EPI correction, MPPCA denoising)
• Diffusion Tensor Imaging (DTI) andDiffusion Kurtosis Imaging (DKI) fit including theMK-curve method;
• Spherical deconvolution using the Constrained Spherical Deconvolution(CSD),Multi-shell CSD,damped Richardson Lucy, theGeneralized Richardson Lucy andmFOD;
• Robust deconvolution of multiple water pools (NNLS/L2NNLS/PL2NNLS) as inDe Luca et al. 2018
• [NEW!] Automatic fiber clustering thanks toWMA pipeline

Please, seethis guide

• I have coded most functions to accept Python-like name/value argument couples. To know which arguments to specify, just try the MATLAB help as, for instance, "help EDTI.LoadNifti"
• The file naming convention is to always indicate Niftis as .nii, even when they are actually compressed in .nii.gz. The code takes care of that, but expects only .nii as arguments in function calls!

Examples of some functionalities can be found inhere

• MRIToolkit relies on a couple of third party dependencies:
  • Elastix:
    • 1. Either compile your own version or grab the executables for your platformhere.
    • 2. Copy the file "TemplateMRIToolkitDefineLocalVars.m" to your MATLAB default folder (user/MATLAB or Documents/MATLAB), rename the file as "MRIToolkitDefineLocalVars.m".
    • 3. Edit the script, adjusting the variable MRIToolkit.Elastix.Location as needed.
  • NODDI toolbox: if you would like to try the mFOD method, you will need to add theNODDI toolbox to the MATLAB path.
  • ExploreDTI: While MRIToolkit is entirely self-sufficient (e.g. all needed ExploreDTI functions are bundled and adapted), the visualization of fiber tractograhy and other results will need ExploreDTI. Get it for free fromAlexander Leemans.

• MRIToolkit is NOT approved for clinical use
• This code is a work in progress. It will be updated without notice to ensure bug-fixes and the inclusion of best available methods

• This software is distributed under the LGPLv3 license (https://opensource.org/licenses/lgpl-3.0.html).

• Magnetic Resonance Imaging (MRI)
• Image segmentation
• T1 quantification, Inversion Recovery
• T2 quantification, spin echo multi echo
• Extended Phase Graphs
• Diffusion MRI (dMRI) - Diffusion Tensor Imaging (DTI) - Diffusion Kurtosis Imaging (DKI)
• dMRI preprocessing - motion correction - eddy currents correction - EPI distortions correction
• Fiber tractography - Constrained Spherical Deconvolution (CSD) - Generalized Richardson Lucy (GRL) - mFOD
• Laplacian fit - Spectral Fit - Robust Deconvolution
• Automatic tractography clustering

• 'ExploreDTIInterface': I am pleased to announce that MRIToolkit now contains, distributes and develops many functions originally developed as part ofExploreDTI. They are here available as a consolidated library and are planned to also become command line tools. A big thank toAlexander Leemans and Ben Jeurissen for this!
  
• 'SphericalDeconvolution': Methods used for two novel deconvolution methods we developed, namely theGeneralized Richardson Lucy andmFOD. Some of the functions here included have been taken fromHardi Tools of Erick Canales-Rodriguez.
• • 'LesionEditor': a graphical user interface to visualise and edit segmentations of 3D MRI images, originally designed for delineation of multiple-sclerosis lesions on fluid attenuated inversion recovery (FLAIR) images. Requires MATLAB R2018a or newer.Documentation coming soon
• 'NiftiIO_basic': Basic Nifti input/output, including code originally written byJimmy Shen
• 'DW_basic': Utilities to load / manipulate / save dMRI data
• 'OptimizationMethods': Classes and functions for numeric optimisation methods
• 'Relaxometry': Classes for T1/T2 quantification using inversion-recovery / spin-echo multi-echo data
• 'ThirdParty': Utilities from third parties used in other scripts. Includes EPG code fromBrian Hargreaves
• 'ImageRegistrations': Image registration utils based onElastix
• 'GettingStarted': Small examples showcasing functionalities of MRIToolkit.

• 'Diffusion_basic': Class for (basic) dMRI quantification

• 'DW_IVIMDTDK_I': Diffusion MRI fit utilities - IVIM, DT, DKI - used inDe Luca et al. 2017

• 'Dicom_utils': Tools for handling unconventional or buggy DICOMs

• 'MixedCodeUtils': 'Useful general purpose functions

• 'MRIfoundation': Classes for MRI sequences abstraction

• 'EPG_simulator': Classes for EPG simulations

• 'dfMRI', Diffusion fMRI utilities used inDe Luca et al. 2019

Alberto De Luca - First published in 2019