MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation

Abstract

MRtrix3 is an open-source, cross-platform software package for medical image processing, analysis and visualization, with a particular emphasis on the investigation of the brain using diffusion MRI. It is implemented using a fast, modular and flexible general-purpose code framework for image data access and manipulation, enabling efficient development of new applications, whilst retaining high computational performance and a consistent command-line interface between applications. In this article, we provide a high-level overview of the features of the MRtrix3 framework and general-purpose image processing applications provided with the software. KeywordsMRI · software · image · processing · visualisation 6 https://github.com/mrtrix3 7

Test-retest reliability and long-term stability of 3-tissue constrained spherical deconvolution methods for analyzing diffusion MRI data

“…While we examined the reliability of WM/GM/CSF-like tissue signal fractions here, other researchers have used response functions representing different tissue compartments when contextually appropriate. Pietsch et al, (2019) applied two different WM response functions representing mature and immature WM in a developing adolescent cohort to observe WM maturation. Mito et al, (2019) proposed to apply a statistical framework of compositional data analysis to analyze the full 3-tissue composition of WM-, GM-and CSF-like signal fractions directly to study microstructure in white matter lesions, following the initial suggestion of moving towards such WM/GM/CSF-like diffusion signal fraction interpretation by Dhollander et al (2017).…”

“…Finally, we upsampled the preprocessed data to 1.3⨉1.3⨉1.3mm 3 isotropic voxels (Greenspan, 2008;Kuklisova-Murgasova et al, 2012;Bastiani et al, 2019). These preprocessing steps are largely similar to those used in other recently published works (Bastiani et al, 2019;Pietsch et al, 2019;Mito et al, 2019;Aerts et al, 2019). Brain masks were obtained for all subjects by performing a recursive application of the Brain Extraction Tool (Avants et al, 2014).…”

Modeling brain dynamics after tumor resection using The Virtual Brain

“…For preprocessing and construction of structural connectomes based on the diffusion MRI (dMRI) data, a processing pipeline was used combining FSL (FMRIB's Software Library; Jenkinson et al, 2012; version 5.0.9) and MRtrix3 (Tournier et al, 2019). Preprocessing steps included correction for various artifacts (noise (Veraart et al, 2016), Gibbs ringing (Kellner, Dhital, Kiselev, & Reisert, 2016), motion and eddy currents (Andersson & Sotiropoulos, 2016), susceptibility induced distortions (Andersson, Skare, & Ashburner, 2003) and bias field inhomogeneities (Zhang, Brady, & Smith, 2001)), registration of subjects' high-resolution anatomical images to diffusion space (Jenkinson, Bannister, Brady, & Smith, 2002;Jenkinson & Smith, 2001), and segmentation of the anatomical images into gray matter, white matter and cerebrospinal fluid (Zhang et al, 2001).…”

“…Preprocessing steps included correction for various artifacts (noise (Veraart et al, 2016), Gibbs ringing (Kellner, Dhital, Kiselev, & Reisert, 2016), motion and eddy currents (Andersson & Sotiropoulos, 2016), susceptibility induced distortions (Andersson, Skare, & Ashburner, 2003) and bias field inhomogeneities (Zhang, Brady, & Smith, 2001)), registration of subjects' high-resolution anatomical images to diffusion space (Jenkinson, Bannister, Brady, & Smith, 2002;Jenkinson & Smith, 2001), and segmentation of the anatomical images into gray matter, white matter and cerebrospinal fluid (Zhang et al, 2001). Further, quantitative whole-brain probabilistic tractography was performed using MRtrix3 (Tournier et al, 2019), resulting in 7.5 million streamlines per subject (more details are available in Aerts et al, 2018). Structural connectivity (SC) matrices were then constructed by transforming each individual's FreeSurfer parcellation scheme to the diffusion MRI data and calculating the number of estimated streamlines between each pair of brain regions.…”

“…Starting from the preprocessed pre-operative dMRI data, a novel technique to model the diffusion signal was applied, named single-shell 3-tissue constrained spherical deconvolution (SS3T-CSD) Dhollander, Raffelt, & Connelly, 2016), using MRtrix3Tissue (https://3Tissue.github.io), a fork of the MRtrix3 software (Tournier et al, 2019). Investigating the performance of this technique for this purpose, we have recently shown that SS3T-CSD allows to reconstruct white matter streamlines within infiltrative tumors and in immediately adjacent tissue (Aerts, Dhollander, & Marinazzo, 2019).…”