List of MRtrix3 commands

Lang

Command

Synopsis

C++

5tt2gmwmi

Generate a mask image appropriate for seeding streamlines on the grey matter-white matter interface

C++

5tt2vis

Generate an image for visualisation purposes from an ACT 5TT segmented anatomical image

C++

5ttcheck

Thoroughly check that one or more images conform to the expected ACT five-tissue-type (5TT) format

C++

5ttedit

Manually set the partial volume fractions in an ACT five-tissue-type (5TT) image using mask images

Python

5ttgen

Generate a 5TT image suitable for ACT

C++

afdconnectivity

Obtain an estimate of fibre connectivity between two regions using AFD and streamlines tractography

C++

amp2response

Estimate response function coefficients based on the DWI signal in single-fibre voxels

C++

amp2sh

Convert a set of amplitudes (defined along a set of corresponding directions) to their spherical harmonic representation

C++

connectome2tck

Extract streamlines from a tractogram based on their assignment to parcellated nodes

C++

connectomeedit

Perform basic operations on a connectome

C++

connectomestats

Connectome group-wise statistics at the edge level using non-parametric permutation testing

C++

dcmedit

Edit DICOM file in-place

C++

dcminfo

Output DICOM fields in human-readable format

C++

dirflip

Invert the polarity of individual directions so as to optimise a unipolar electrostatic repulsion model

C++

dirgen

Generate a set of uniformly distributed directions using a bipolar electrostatic repulsion model

C++

dirmerge

Splice / merge multiple sets of directions in such a way as to maintain near-optimality upon truncation

C++

dirorder

Reorder a set of directions to ensure near-uniformity upon truncation

C++

dirsplit

Split a set of evenly distributed directions (as generated by dirgen) into approximately uniformly distributed subsets

C++

dirstat

Report statistics on a direction set

C++

dwi2adc

Convert mean dwi (trace-weighted) images to mean ADC maps

C++

dwi2fod

Estimate fibre orientation distributions from diffusion data using spherical deconvolution

C++

dwi2mask

Generates a whole brain mask from a DWI image

Python

dwi2response

Estimate response function(s) for spherical deconvolution

C++

dwi2tensor

Diffusion (kurtosis) tensor estimation

Python

dwibiascorrect

Perform B1 field inhomogeneity correction for a DWI volume series

Python

dwicat

Concatenating multiple DWI series accounting for differential intensity scaling

C++

dwidenoise

dMRI noise level estimation and denoising using Marchenko-Pastur PCA

C++

dwiextract

Extract diffusion-weighted volumes, b=0 volumes, or certain shells from a DWI dataset

Python

dwifslpreproc

Perform diffusion image pre-processing using FSL’s eddy tool; including inhomogeneity distortion correction using FSL’s topup tool if possible

Python

dwigradcheck

Check the orientation of the diffusion gradient table

Python

dwinormalise

Perform various forms of intensity normalisation of DWIs

Python

dwishellmath

Apply an mrmath operation to each b-value shell in a DWI series

C++

fixel2peaks

Convert data in the fixel directory format into a 4D image of 3-vectors

C++

fixel2sh

Convert a fixel-based sparse-data image into an spherical harmonic image

C++

fixel2tsf

Map fixel values to a track scalar file based on an input tractogram

C++

fixel2voxel

Convert a fixel-based sparse-data image into some form of scalar image

C++

fixelcfestats

Fixel-based analysis using connectivity-based fixel enhancement and non-parametric permutation testing

C++

fixelconnectivity

Generate a fixel-fixel connectivity matrix

C++

fixelconvert

Convert between the old format fixel image (.msf / .msh) and the new fixel directory format

C++

fixelcorrespondence

Obtain fixel-fixel correpondence between a subject fixel image and a template fixel mask

C++

fixelcrop

Crop/remove fixels from sparse fixel image using a binary fixel mask

C++

fixelfilter

Perform filtering operations on fixel-based data

C++

fixelreorient

Reorient fixel directions

C++

fod2dec

Generate FOD-based DEC maps, with optional panchromatic sharpening and/or luminance/perception correction

C++

fod2fixel

Perform segmentation of continuous Fibre Orientation Distributions (FODs) to produce discrete fixels

Python

for_each

Perform some arbitrary processing step for each of a set of inputs

C++

label2colour

Convert a parcellated image (where values are node indices) into a colour image

C++

label2mesh

Generate meshes from a label image

C++

labelconvert

Convert a connectome node image from one lookup table to another

Python

labelsgmfix

In a FreeSurfer parcellation image, replace the sub-cortical grey matter structure delineations using FSL FIRST

C++

labelstats

Compute statistics of parcels within a label image

C++

maskdump

Print out the locations of all non-zero voxels in a mask image

C++

maskfilter

Perform filtering operations on 3D / 4D mask images

C++

mesh2voxel

Convert a mesh surface to a partial volume estimation image

C++

meshconvert

Convert meshes between different formats, and apply transformations

C++

meshfilter

Apply filter operations to meshes

C++

mraverageheader

Calculate the average (unbiased) coordinate space of all input images

C++

mrcalc

Apply generic voxel-wise mathematical operations to images

C++

mrcat

Concatenate several images into one

C++

mrcentroid

Determine the centre of mass / centre of gravity of an image

C++

mrcheckerboardmask

Create bitwise checkerboard image

C++

mrclusterstats

Voxel-based analysis using permutation testing and threshold-free cluster enhancement

C++

mrcolour

Apply a colour map to an image

C++

mrconvert

Perform conversion between different file types and optionally extract a subset of the input image

C++

mrdegibbs

Remove Gibbs Ringing Artifacts

C++

mrdump

Print out the values within an image

C++

mredit

Directly edit the intensities within an image from the command-line

C++

mrfilter

Perform filtering operations on 3D / 4D MR images

C++

mrgrid

Modify the grid of an image without interpolation (cropping or padding) or by regridding to an image grid with modified orientation, location and or resolution. The image content remains in place in real world coordinates.

C++

mrhistmatch

Modify the intensities of one image to match the histogram of another

C++

mrhistogram

Generate a histogram of image intensities

C++

mrinfo

Display image header information, or extract specific information from the header

C++

mrmath

Compute summary statistic on image intensities either across images, or along a specified axis of a single image

C++

mrmetric

Computes a dissimilarity metric between two images

C++

mrregister

Register two images together using a symmetric rigid, affine or non-linear transformation model

C++

mrstats

Compute images statistics

C++

mrthreshold

Create bitwise image by thresholding image intensity

C++

mrtransform

Apply spatial transformations to an image

Python

mrtrix_cleanup

Clean up residual temporary files & scratch directories from MRtrix3 commands

C++

mrview

The MRtrix image viewer

C++

mtnormalise

Multi-tissue informed log-domain intensity normalisation

C++

peaks2amp

Extract amplitudes from a peak directions image

C++

peaks2fixel

Convert peak directions image to a fixel directory

Python

population_template

Generates an unbiased group-average template from a series of images

Python

responsemean

Calculate the mean response function from a set of text files

C++

sh2amp

Evaluate the amplitude of an image of spherical harmonic functions along specified directions

C++

sh2peaks

Extract the peaks of a spherical harmonic function in each voxel

C++

sh2power

Compute the total power of a spherical harmonics image

C++

sh2response

Generate an appropriate response function from the image data for spherical deconvolution

C++

shbasis

Examine the values in spherical harmonic images to estimate (and optionally change) the SH basis used

C++

shconv

Perform spherical convolution

C++

shview

View spherical harmonics surface plots

C++

tck2connectome

Generate a connectome matrix from a streamlines file and a node parcellation image

C++

tck2fixel

Compute a fixel TDI map from a tractogram

C++

tckconvert

Convert between different track file formats

C++

tckdfc

Perform the Track-Weighted Dynamic Functional Connectivity (TW-dFC) method

C++

tckedit

Perform various editing operations on track files

C++

tckgen

Perform streamlines tractography

C++

tckglobal

Multi-Shell Multi-Tissue Global Tractography

C++

tckinfo

Print out information about a track file

C++

tckmap

Use track data as a form of contrast for producing a high-resolution image

C++

tckresample

Resample each streamline in a track file to a new set of vertices

C++

tcksample

Sample values of an associated image along tracks

C++

tcksift

Filter a whole-brain fibre-tracking data set such that the streamline densities match the FOD lobe integrals

C++

tcksift2

Optimise per-streamline cross-section multipliers to match a whole-brain tractogram to fixel-wise fibre densities

C++

tckstats

Calculate statistics on streamlines lengths

C++

tcktransform

Apply a spatial transformation to a tracks file

C++

tensor2metric

Generate maps of tensor-derived parameters

C++

transformcalc

Perform calculations on linear transformation matrices

C++

transformcompose

Compose any number of linear transformations and/or warps into a single transformation

C++

transformconvert

Convert linear transformation matrices

C++

tsfdivide

Divide corresponding values in track scalar files

C++

tsfinfo

Print out information about a track scalar file

C++

tsfmult

Multiply corresponding values in track scalar files

C++

tsfsmooth

Gaussian filter a track scalar file

C++

tsfthreshold

Threshold and invert track scalar files

C++

tsfvalidate

Validate a track scalar file against the corresponding track data

C++

vectorstats

Statistical testing of vector data using non-parametric permutation testing

C++

voxel2fixel

Map the scalar value in each voxel to all fixels within that voxel

C++

voxel2mesh

Generate a surface mesh representation from a voxel image

C++

warp2metric

Compute fixel-wise or voxel-wise metrics from a 4D deformation field

C++

warpconvert

Convert between different representations of a non-linear warp

C++

warpcorrect

Replaces voxels in a deformation field that point to a specific out of bounds location with nan,nan,nan

C++

warpinit

Create an initial warp image, representing an identity transformation

C++

warpinvert

Invert a non-linear warp field