MRtrix: Part 3#
Registration and Streamline Fitting#
This notebook is the third in a series on Diffusion Analysis using MRtrix and builds upon the data generated in MRtrix: Part 2 - Constrained Spherical Deconvolution and Tissue estimation. The data necessary for this example will be downloaded from OSF. This notebook will focus on coregistration of the diffusion and anatomical images and generation of streamlines, building on the concepts introduced earlier.
Author: Monika Doerig
Date: 5 June 2025
License:
Note: If this notebook uses neuroimaging tools from Neurocontainers, those tools retain their original licenses. Please see Neurodesk citation guidelines for details.
Citation and Resources:#
Tools included in this workflow#
MRtrix3:
Tournier, J.-D.; Smith, R. E.; Raffelt, D.; Tabbara, R.; Dhollander, T.; Pietsch, M.; Christiaens, D.; Jeurissen, B.; Yeh, C.-H. & Connelly, A. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage, 2019, 202, 116137. https://doi.org/10.1016/j.neuroimage.2019.116137
For more details: https://www.mrtrix.org/
FSL:
Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012). FSL. NeuroImage, 62(2), 782–790. https://doi.org/10.1016/j.neuroimage.2011.09.015
FURY - Free Unified Rendering in Python:
Eleftherios Garyfallidis, Serge Koudoro, Javier Guaje, Marc-Alexandre Côté, Soham Biswas, David Reagan, Nasim Anousheh, Filipi Silva, Geoffrey Fox, and Fury Contributors. “FURY: advanced scientific visualization.” Journal of Open Source Software 6, no. 64 (2021): 3384. https://doi.org/10.21105/joss.03384
DIPY:
Garyfallidis, E., Brett, M., Amirbekian, B., Rokem, A., van der Walt, S., Descoteaux, M., Nimmo-Smith, I., & Dipy Contributors (2014). Dipy, a library for the analysis of diffusion MRI data. Frontiers in neuroinformatics, 8, 8. https://doi.org/10.3389/fninf.2014.00008
Educational resources#
Andy’s Brain Book:
This MRtrix example is based on the Diffusion Analysis with MRtrix chapter from Andy’s Brain Book (Jahn, 2022. doi:10.5281/zenodo.5879293)
Dataset#
Original Data from OpenNeuro:
Hannelore Aerts and Daniele Marinazzo (2018). BTC_preop. OpenNeuro Dataset ds001226
Diffusion Data from the Previous Notebook (Available on OSF):
Dörig, M. (2024, November 19). Diffusion MRI Analysis with MRtrix: An Interactive Three-Part Series on Neurodesk. Retrieved from OSF
Import Python Libraries#
%%capture
! pip install fury==0.11.0 dipy==1.11.0 vtk==9.5.2
import os
import subprocess
import nibabel as nib
import numpy as np
import matplotlib.pyplot as plt
import tempfile
from ipyniivue import NiiVue, SliceType
from IPython.display import display, Markdown, Image
import ipywidgets as widgets
from dipy.viz import actor, colormap as cmap, window
import random
Load modules#
import module
await module.load('mrtrix3/3.0.4')
await module.load('fsl/6.0.7.19')
await module.list()
['mrtrix3/3.0.4', 'fsl/6.0.7.19']
Try typing one of the commands from the library, such as mrconvert. If MRtrix has been installed correctly, you should see the help page printed by default when no arguments are passed to the command:
! mrconvert 2>&1 | head -n 18
MRtrix 3.0.4 mmrrccoonnvveerrtt Mar 20 2024
mmrrccoonnvveerrtt: part of the MRtrix3 package
SSYYNNOOPPSSIISS
Perform conversion between different file types and optionally extract a
subset of the input image
UUSSAAGGEE
_m_r_c_o_n_v_e_r_t [ options ] input output
_i_n_p_u_t the input image.
_o_u_t_p_u_t the output image.
Downloading the data from MRtrix Part 2#
def fetch_if_missing(remote, local):
if not os.path.exists(local):
os.system(f"osf -p y2dq4 fetch {remote} {local}")
else:
print(f"Already exists, skipping: {local}")
os.makedirs("MRtrix_fod", exist_ok=True)
fetch_if_missing("fod/5tt_nocoreg.mif", "MRtrix_fod/5tt_nocoreg.mif")
fetch_if_missing("fod/csffod_norm.mif", "MRtrix_fod/csffod_norm.mif")
fetch_if_missing("fod/gmfod_norm.mif", "MRtrix_fod/gmfod_norm.mif")
fetch_if_missing("fod/wmfod_norm.mif", "MRtrix_fod/wmfod_norm.mif")
fetch_if_missing("fod/sub-02_den_preproc_unbiased.mif", "MRtrix_fod/sub-02_den_preproc_unbiased.mif")
Already exists, skipping: MRtrix_fod/5tt_nocoreg.mif
Already exists, skipping: MRtrix_fod/csffod_norm.mif
Already exists, skipping: MRtrix_fod/gmfod_norm.mif
Already exists, skipping: MRtrix_fod/wmfod_norm.mif
Already exists, skipping: MRtrix_fod/sub-02_den_preproc_unbiased.mif
Coregistering the Diffusion and Anatomical Images#
Our next step is to coregister the anatomical and diffusion-weighted images. This ensures that the boundaries of the tissue types are aligned with the boundaries of the diffusion-weighted images; even small differences in the location of the two scans can throw off the tractography results.
We will first use the commands dwiextract and mrmath to average together the B0 images from the diffusion data. These are the images that look most like T2-weighted functional scans, since a diffusion gradient wasn’t applied during their acquisition - in other words, they were acquired with a b-value of zero. To see how this works, type the following command:
! dwiextract ./MRtrix_fod/sub-02_den_preproc_unbiased.mif - -bzero | mrmath - mean ./MRtrix_fod/mean_b0.mif -axis 3 -force
mrmath: [WARNING] existing output files will be overwritten
dwiextract: [100%] extracting volumes...?7h
dwiextract: [100%] extracting volumes
mrmath: [100%] preloading data for "/tmp/mrtrix-tmp-qHPd4i.mif"
mrmath: [100%] computing mean along axis 3......?7h
mrmath: [100%] computing mean along axis 3...
#
There are two parts to this command, separated by a pipe (”|”). The left half of the command, dwiextract, takes the preprocessed diffusion-weighted image as an input, and the -bzero option extracts the B0 images; the solitary - argument indicates that the output should be used as input for the second part of the command, to the right of the pipe. mrmath then takes these output B0 images and computes the mean along the 3rd axis, or the time dimension. In other words, if we start with an index of 0, then the number 3 indicates the 4th dimension, which simply means to average over all of the volumes.
In order to carry out the coregistration between the diffusion and anatomical images, we will need to take a brief detour outside of MRtrix. The software package doesn’t have a coregistration command in its library, so we will need to use another software package’s commands instead. Although you can choose any one you want, we will focus here on FSL’s flirt command.
The first step is to convert both the segmented anatomical image and the B0 images we just extracted:
! mrconvert ./MRtrix_fod/mean_b0.mif ./MRtrix_fod/mean_b0.nii.gz -force
! mrconvert ./MRtrix_fod/5tt_nocoreg.mif ./MRtrix_fod/5tt_nocoreg.nii.gz -force
mrconvert: [WARNING] existing output files will be overwritten
mrconvert: [100%] copying from "./MRtrix_fod/mean_b0.mif" to "./MRtrix_fod/mean_b0.nii.gz"
mrconvert: [ 50%] compressing image "./MRtrix_fod/mean_b0.nii.gz"...?7h
mrconvert: [100%] compressing image "./MRtrix_fod/mean_b0.nii.gz"
mrconvert: [WARNING] existing output files will be overwritten
mrconvert: [ 3%] copying from "./MRtrix_fod/5tt_nocoreg.mif" to "./MRtrix_fod/5tt_nocoreg.nii.gz"...?7h
mrconvert: [ 16%] copying from "./MRtrix_fod/5tt_nocoreg.mif" to "./MRtrix_fod/5tt_nocoreg.nii.gz"...?7h
mrconvert: [ 51%] copying from "./MRtrix_fod/5tt_nocoreg.mif" to "./MRtrix_fod/5tt_nocoreg.nii.gz"...?7h
mrconvert: [100%] copying from "./MRtrix_fod/5tt_nocoreg.mif" to "./MRtrix_fod/5tt_nocoreg.nii.gz"...?7h
mrconvert: [100%] copying from "./MRtrix_fod/5tt_nocoreg.mif" to "./MRtrix_fod/5tt_nocoreg.nii.gz"
mrconvert: [ 4%] compressing image "./MRtrix_fod/5tt_nocoreg.nii.gz"...?7h
mrconvert: [ 8%] compressing image "./MRtrix_fod/5tt_nocoreg.nii.gz"...?7h
mrconvert: [ 11%] compressing image "./MRtrix_fod/5tt_nocoreg.nii.gz"...?7h
mrconvert: [ 15%] compressing image "./MRtrix_fod/5tt_nocoreg.nii.gz"...?7h
mrconvert: [ 23%] compressing image "./MRtrix_fod/5tt_nocoreg.nii.gz"...?7h
mrconvert: [ 32%] compressing image "./MRtrix_fod/5tt_nocoreg.nii.gz"...?7h
mrconvert: [ 41%] compressing image "./MRtrix_fod/5tt_nocoreg.nii.gz"...?7h
mrconvert: [ 46%] compressing image "./MRtrix_fod/5tt_nocoreg.nii.gz"...?7h
mrconvert: [ 51%] compressing image "./MRtrix_fod/5tt_nocoreg.nii.gz"...?7h
mrconvert: [ 57%] compressing image "./MRtrix_fod/5tt_nocoreg.nii.gz"...?7h
mrconvert: [ 65%] compressing image "./MRtrix_fod/5tt_nocoreg.nii.gz"...?7h
mrconvert: [ 71%] compressing image "./MRtrix_fod/5tt_nocoreg.nii.gz"...?7h
mrconvert: [ 78%] compressing image "./MRtrix_fod/5tt_nocoreg.nii.gz"...?7h
mrconvert: [ 92%] compressing image "./MRtrix_fod/5tt_nocoreg.nii.gz"...?7h
mrconvert: [100%] compressing image "./MRtrix_fod/5tt_nocoreg.nii.gz"
#
Since flirt can only work with a single 3D image (not 4D datasets), we will use fslroi to extract the first volume of the segmented dataset, which corresponds to the Grey Matter segmentation
! fslroi ./MRtrix_fod/5tt_nocoreg.nii.gz ./MRtrix_fod/5tt_vol0.nii.gz 0 1
We then use the flirt command to coregister the two datasets:
! flirt -in ./MRtrix_fod/mean_b0.nii.gz -ref ./MRtrix_fod/5tt_vol0.nii.gz -interp nearestneighbour -dof 6 -omat ./MRtrix_fod/diff2struct_fsl.mat
This command uses the grey matter segmentation (i.e., “5tt_vol0.nii.gz”) as the reference image, meaning that it stays stationary. The averaged B0 images are then moved to find the best fit with the grey matter segmentation. The output of this command, “diff2struct_fsl.mat”, contains the transformation matrix that was used to overlay the diffusion image on top of the grey matter segmentation.
Now that we have generated our transformation matrix, we will need to convert it into a format that can be read by MRtrix. That is, we are now ready to travel back into MRtrix after briefly stepping outside of it. The command transformconvert does this:
! transformconvert ./MRtrix_fod/diff2struct_fsl.mat ./MRtrix_fod/mean_b0.nii.gz ./MRtrix_fod/5tt_nocoreg.nii.gz flirt_import ./MRtrix_fod/diff2struct_mrtrix.txt -force
transformconvert: [WARNING] existing output files will be overwritten
Note that the above steps used the anatomical segmentation as the reference image. We did this because usually the coregistration is more accurate if the reference image has higher spatial resolution and sharper distinction between the tissue types. However, we also want to introduce as few edits and interpolations to the functional data as possible during preprocessing. Therefore, since we already have the steps to transform the diffusion image to the anatomical image, we can take the inverse of the transformation matrix to do the opposite - i.e., coregister the anatomical image to the diffusion image:
! mrtransform ./MRtrix_fod/5tt_nocoreg.mif -linear ./MRtrix_fod/diff2struct_mrtrix.txt -inverse ./MRtrix_fod/5tt_coreg.mif -force
mrtransform: [WARNING] existing output files will be overwritten
mrtransform: [ 3%] copying from "./MRtrix_fod/5tt_nocoreg.mif" to "./MRtrix_fod/5tt_coreg.mif"......?7h
mrtransform: [ 16%] copying from "./MRtrix_fod/5tt_nocoreg.mif" to "./MRtrix_fod/5tt_coreg.mif"......?7h
mrtransform: [ 29%] copying from "./MRtrix_fod/5tt_nocoreg.mif" to "./MRtrix_fod/5tt_coreg.mif"......?7h
mrtransform: [ 44%] copying from "./MRtrix_fod/5tt_nocoreg.mif" to "./MRtrix_fod/5tt_coreg.mif"......?7h
mrtransform: [ 57%] copying from "./MRtrix_fod/5tt_nocoreg.mif" to "./MRtrix_fod/5tt_coreg.mif"......?7h
mrtransform: [ 74%] copying from "./MRtrix_fod/5tt_nocoreg.mif" to "./MRtrix_fod/5tt_coreg.mif"......?7h
mrtransform: [ 91%] copying from "./MRtrix_fod/5tt_nocoreg.mif" to "./MRtrix_fod/5tt_coreg.mif"......?7h
mrtransform: [100%] copying from "./MRtrix_fod/5tt_nocoreg.mif" to "./MRtrix_fod/5tt_coreg.mif"...
The resulting file, “5tt_coreg.mif”, can be loaded into mrview in order to examine the quality of the coregistration. This is the command to visualizie it with mrview:
mrview sub-02_den_preproc_unbiased.mif -overlay.load 5tt_nocoreg.mif -overlay.colourmap 2 -overlay.load 5tt_coreg.mif -overlay.colourmap 1
The “overlay.colourmap” options specify different color codes for each image that is loaded. In this case, the boundaries before coregistration will be depicted in blue, and the boundaries after coregistration will be shown in red. The change in the boundaries before and after coregistration may be very slight, but they will have a large effect on the rest of the steps that we do. Make sure to check the boundaries in all three views; you can also use the Tool -> Overlay menu to display or hide the different overlays.
We visualize the gray matter coregistration result with ipyniivue. To reduce file size while maintaining anatomical detail, we extract and display only the gray matter tissue from the 5-tissue-type segmentation. The unregistered gray matter is depicted in gray, the registered gray matter in red:
# Extract just GM (volume 0) from 5tt
! mrconvert ./MRtrix_fod/5tt_nocoreg.mif -coord 3 0 ./MRtrix_fod/5tt_nocoreg_gm.mif -force
! mrconvert ./MRtrix_fod/5tt_coreg.mif -coord 3 0 ./MRtrix_fod/5tt_coreg_gm.mif -force
mrconvert: [WARNING] existing output files will be overwritten
mrconvert: [ 28%] copying from "./MRtrix_fod/5tt_nocoreg.mif" to "./MRtrix_fod/5tt_nocoreg_gm.mif"...?7h
mrconvert: [ 52%] copying from "./MRtrix_fod/5tt_nocoreg.mif" to "./MRtrix_fod/5tt_nocoreg_gm.mif"...?7h
mrconvert: [100%] copying from "./MRtrix_fod/5tt_nocoreg.mif" to "./MRtrix_fod/5tt_nocoreg_gm.mif"
mrconvert: [WARNING] existing output files will be overwritten
mrconvert: [100%] copying from "./MRtrix_fod/5tt_coreg.mif" to "./MRtrix_fod/5tt_coreg_gm.mif"
⚠️ Note:
ipywidgets
that rely on Python callbacks
(observe,
on_click)
require a running kernel and do not function in the static HTML version of this notebook.
Widgets using client-side trait linking
(jslink)
remain interactive without a kernel.
# Define the volumes with fixed colormaps
volumes = [
{"url": "https://huggingface.co/datasets/neurodeskorg/neurodeskedu/resolve/main/data/examples/diffusion_imaging/MRtrix_3/5tt_nocoreg_gm_f09473fc07f6.mif", "colormap": "gray", "opacity": 1.0},
{"url": "https://huggingface.co/datasets/neurodeskorg/neurodeskedu/resolve/main/data/examples/diffusion_imaging/MRtrix_3/5tt_coreg_gm_8ee71505c3a7.mif", "colormap": "red", "opacity": 1.0},
]
# Create the NiiVue viewer
nv = NiiVue()
nv.load_volumes(volumes)
nv.opts.crosshair_color = [1,1,0,1]
# Slice type widget (Axial, Coronal, Sagittal, Multiplanar)
widget_slice_type = widgets.RadioButtons(
options=[('Axial', 0), ('Coronal', 1), ('Sagittal', 2), ('Multiplanar', 3)],
value=3, # Default to Multiplanar
description='Slice Type:',
style={'description_width': 'initial'},
layout={'width': 'auto'}
)
# Function to update slice type
def update_slice_type(change):
nv.slice_type = change.new
widget_slice_type.observe(update_slice_type, names='value')
# Unregistered image opacity widget
widget_unreg_opacity = widgets.FloatSlider(
value=1.0, min=0.0, max=1.0, step=0.1,
description='Unregistered Image Opacity:',
orientation='horizontal',
style={'description_width': 'initial'},
layout={'width': '400px'}
)
# Function to update unregistered image opacity
def update_unreg_opacity(change):
nv.volumes[0].opacity = change.new
widget_unreg_opacity.observe(update_unreg_opacity, names='value')
# Registered image opacity widget
widget_reg_opacity = widgets.FloatSlider(
value=1.0, min=0.0, max=1.0, step=0.1,
description='Registered Image Opacity:',
orientation='horizontal',
style={'description_width': 'initial'},
layout={'width': '400px'}
)
# Function to update registered image opacity
def update_reg_opacity(change):
nv.volumes[1].opacity = change.new
widget_reg_opacity.observe(update_reg_opacity, names='value')
# Arrange widgets in a vertical box for better layout
widgets_box = widgets.VBox([widget_slice_type, widget_unreg_opacity, widget_reg_opacity])
# Display the viewer and widgets
display(widgets_box, nv)
The last step to create the “seed” boundary - the boundary separating the grey from the white matter, which we will use to create the seeds for our streamlines - is created with the command 5tt2gmwmi (which stands for “5 Tissue Type (segmentation) to Grey Matter / White Matter Interface).
! 5tt2gmwmi ./MRtrix_fod/5tt_coreg.mif ./MRtrix_fod/gmwmSeed_coreg.mif -force
5tt2gmwmi: [WARNING] existing output files will be overwritten
5tt2gmwmi: [ 6%] Generating GMWMI seed mask...?7h
5tt2gmwmi: [ 16%] Generating GMWMI seed mask...?7h
5tt2gmwmi: [ 44%] Generating GMWMI seed mask...?7h
5tt2gmwmi: [ 83%] Generating GMWMI seed mask...?7h
5tt2gmwmi: [100%] Generating GMWMI seed mask
Again, we will check the result with mrview to make sure the interface is where we think it should be. This is the mrviewcommand:
mrview sub-02_den_preproc_unbiased.mif -overlay.load gmwmSeed_coreg.mif
We visualize the grey/white matter boundary mask (green) that will serve as the seeding region for tractography, overlaid on the coregistered grey matter (gray) with ipyniivue:
nv2 = NiiVue(back_color=(0, 0, 0, 1))
nv2.opts.is_alpha_clip_dark = True
nv2.load_volumes([
{"url": "https://huggingface.co/datasets/neurodeskorg/neurodeskedu/resolve/main/data/examples/diffusion_imaging/MRtrix_3/5tt_coreg_gm_8ee71505c3a7.mif", "colormap": "gray", "opacity": 0.5}, # GM
{"url": "https://huggingface.co/datasets/neurodeskorg/neurodeskedu/resolve/main/data/examples/diffusion_imaging/MRtrix_3/gmwmSeed_coreg_746969289733.mif", "colormap": "green"}, # GM/WM boundary
])
nv2
Now that we have determined where the boundary is between the grey matter and the white matter, we are ready to begin generating streamlines in order to reconstruct the major white matter pathways of the brain.
Streamlines#
Having created the interface between the white matter and the grey matter, we are ready to generate streamlines - threads that connect anatomically distinct regions of grey matter. These are estimates of the underlying white matter tracts, and MRtrix uses a probabilistic approach to do this; a large number of streamlines are generated for each voxel of the grey matter / white matter boundary, and then those streamlines are culled based on different criteria that we specify. We will discuss some of the most popular options below.
Anatomically Constrained Tractography#
One of MRtrix’s features is Anatomically Constrained Tractography, or ACT. This method will only determine that a streamline is valid if it is biologically plausible. For example, a streamline that terminates in the cerebrospinal fluid will be discarded, since white matter tracts tend to both originate and terminate in grey matter. In other words, the streamlines will be constrained to the white matter. The effect of either including or omitting this step can be seen in the following figure:
An analysis without (left) and with (right) anatomically constrained tractography. Note how without ACT, the streamlines do tend to congregate within the white matter; however, a large number of them are found within the grey matter and cerebrospinal fluid. Using ACT (right) restricts the streamlines to the white matter tracts that we will want to analyze.
Anatomically constrained tractography isn’t a separate preprocessing step, but rather an option that can be included with the command tckgen, which generates the actual streamlines.
Generating Streamlines with tckgen#
MRtrix is able to do both deterministic and probabilistic tractography. In deterministic tractography, the direction of the streamline at each voxel is determined based on the predominant fiber orientation; in other words, the streamline is determined by a single parameter. MRtrix includes multiple options to do this type of deterministic tractography, such as FAC or tensor_det.
The other method, probabilistic tractography, is the default in MRtrix. In this approach, multiple streamlines are generated from seed regions all along the boundary between the grey matter and white matter. The direction of the streamline will most likely follow the predominant fiber orientation density, but not always; due to a large number of samples, some streamlines will follow other directions. This becomes less likely if the FOD is extremely strong in one direction - for example, the FODs within a structure such as the corpus callosum will tend to all be aligned left-to-right - but the sampling becomes more diverse in regions that do not have a predominant fiber orientation.
The default method is to use an algorithm known as iFOD2, which will use a probabilistic streamline approach. Other algorithms can be found at this site, although for the remainder of this example we will use the default of iFOD2.
How Many Streamlines?#
There is a trade-off between the number of generated streamlines and the amount of time that it takes. More streamlines result in a more accurate reconstruction of the underlying white-matter tracts, but estimating a large number of them can take a prohibitively long time.
The “correct” number of streamlines to use is still being debated, but at least 10 million should be a good starting place. However, for computational reasons we will only compute 1 million in this tutorial:
! tckgen -act ./MRtrix_fod/5tt_coreg.mif -backtrack -seed_gmwmi ./MRtrix_fod/gmwmSeed_coreg.mif -nthreads 20 -maxlength 250 -cutoff 0.06 -select 1000000 ./MRtrix_fod/wmfod_norm.mif ./MRtrix_fod/tracks_1M.tck -force
tckgen: [WARNING] existing output files will be overwritten
tckgen: [ 0%] 6 seeds, 4 streamlines, 2 selected?7h
tckgen: [ 0%] 591 seeds, 483 streamlines, 169 selected?7h
tckgen: [ 0%] 1711 seeds, 1409 streamlines, 505 selected?7h
tckgen: [ 0%] 2841 seeds, 2357 streamlines, 878 selected?7h
tckgen: [ 0%] 3921 seeds, 3234 streamlines, 1258 selected?7h
tckgen: [ 0%] 5001 seeds, 4110 streamlines, 1634 selected?7h
tckgen: [ 0%] 6141 seeds, 5046 streamlines, 2015 selected?7h
tckgen: [ 0%] 7421 seeds, 6114 streamlines, 2472 selected?7h
tckgen: [ 0%] 8621 seeds, 7103 streamlines, 2865 selected?7h
tckgen: [ 0%] 9851 seeds, 8109 streamlines, 3256 selected?7h
tckgen: [ 0%] 10941 seeds, 9000 streamlines, 3628 selected?7h
tckgen: [ 0%] 12011 seeds, 9883 streamlines, 3997 selected?7h
tckgen: [ 0%] 13281 seeds, 10957 streamlines, 4420 selected?7h
tckgen: [ 0%] 14381 seeds, 11864 streamlines, 4783 selected?7h
tckgen: [ 0%] 15731 seeds, 12984 streamlines, 5248 selected?7h
tckgen: [ 0%] 17041 seeds, 14066 streamlines, 5679 selected?7h
tckgen: [ 0%] 18151 seeds, 14987 streamlines, 6057 selected?7h
tckgen: [ 0%] 19561 seeds, 16150 streamlines, 6499 selected?7h
tckgen: [ 0%] 20641 seeds, 17039 streamlines, 6864 selected?7h
tckgen: [ 0%] 21661 seeds, 17873 streamlines, 7213 selected?7h
tckgen: [ 0%] 22681 seeds, 18695 streamlines, 7564 selected?7h
tckgen: [ 0%] 23659 seeds, 19499 streamlines, 7906 selected?7h
tckgen: [ 0%] 24763 seeds, 20417 streamlines, 8282 selected?7h
tckgen: [ 0%] 25971 seeds, 21403 streamlines, 8687 selected?7h
tckgen: [ 0%] 27172 seeds, 22423 streamlines, 9097 selected?7h
tckgen: [ 0%] 28381 seeds, 23399 streamlines, 9514 selected?7h
tckgen: [ 0%] 29585 seeds, 24381 streamlines, 9896 selected?7h
tckgen: [ 1%] 29905 seeds, 24636 streamlines, 10000 selected?7h
tckgen: [ 1%] 30891 seeds, 25450 streamlines, 10356 selected?7h
tckgen: [ 1%] 32072 seeds, 26415 streamlines, 10770 selected?7h
tckgen: [ 1%] 33144 seeds, 27287 streamlines, 11114 selected?7h
tckgen: [ 1%] 34151 seeds, 28106 streamlines, 11484 selected?7h
tckgen: [ 1%] 35041 seeds, 28843 streamlines, 11777 selected?7h
tckgen: [ 1%] 35951 seeds, 29574 streamlines, 12086 selected?7h
tckgen: [ 1%] 37151 seeds, 30541 streamlines, 12485 selected?7h
tckgen: [ 1%] 38071 seeds, 31285 streamlines, 12812 selected?7h
tckgen: [ 1%] 39111 seeds, 32135 streamlines, 13158 selected?7h
tckgen: [ 1%] 40261 seeds, 33095 streamlines, 13508 selected?7h
tckgen: [ 1%] 41201 seeds, 33862 streamlines, 13821 selected?7h
tckgen: [ 1%] 42321 seeds, 34783 streamlines, 14189 selected?7h
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tckgen: [ 2%] 59397 seeds, 48790 streamlines, 20000 selected?7h
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tckgen: [100%] 2965942 seeds, 2436193 streamlines, 1000000 selected
In this command, the “-act” option specifies that we will use the anatomically-segmented image to constrain our analysis to the white matter. “-backtrack” indicates for the current streamline to go back and run the same streamline again if it terminates in a strange place (e.g., the cerebrospinal fluid); “-maxlength” sets the maximum tract length, in voxels, that will be permitted; and “-cutoff” specifies the FOD amplitude for terminating a tract (for example, a value of 0.06 would not permit a streamline to go along an FOD that is lower than that number). “-seed_gmwmi” takes as an input the grey-matter / white-matter boundary that was generated using the 5tt2gmwmi command.
“-nthreads” specifies the number of processing cores you wish to use, in order to speed up the analysis. And finally, “-select” indicates how many total streamlines to generate. Note that a shorthand can be used if you like; instead of, 10000000, you can rewrite it as 10000k (meaning “ten thousand thousands”, which equals “ten million”). The last two arguments specify both the input (wmfod_norm.mif) and a label for the output (tracks_1M.tck).
If you want to visualize the output, extract a subset of the output by using tckedit:
! tckedit ./MRtrix_fod/tracks_1M.tck -number 10k ./MRtrix_fod/tracks_10k.tck -force
tckedit: [WARNING] existing output files will be overwritten
tckedit: [. ] 1 read, 1 written?7h
tckedit: [done] 10000 read, 10000 written
This can then be loaded into mrview by using the “-tractography.load” option, which will automatically overlay the tracks_10k.tck file onto the preprocessed diffusion-weighted image:
mrview sub-02_den_preproc_unbiased.mif -tractography.load tracks_10k.tck
Below, we demonstrate three visualization options — Matplotlib, DIPY, and ipyniivue — to show how tractography output can be explored using different tools.
Matplotlib#
# Load the .tck file using nibabel
tractogram = nib.streamlines.load("./MRtrix_fod/tracks_10k.tck")
streamlines = tractogram.streamlines
streamlines = list(streamlines)
# Define the fraction of streamlines to display (e.g., 80%)
display_fraction = 0.8
# Randomly select a subset of streamlines
num_streamlines = len(streamlines)
subsampled_streamlines = random.sample(streamlines, int(display_fraction * num_streamlines))
streamlines_to_display = subsampled_streamlines # to visualize the whole sample use streamlines instead of subsampled_streamlines
# Use DIPY's colormap for streamline coloring based on orientation
colors = cmap.line_colors(streamlines_to_display)
# Set up the plot with 3 subplots
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
# Plot Axial slice
for streamline, color in zip(streamlines_to_display, colors):
axes[0].plot(streamline[:, 0], streamline[:, 1], color=color, lw=0.5)
axes[0].axis("off")
axes[0].set_title("Axial Slice")
axes[0].axis("equal")
# Plot Coronal slice
for streamline, color in zip(streamlines_to_display, colors):
axes[1].plot(streamline[:, 0], streamline[:, 2], color=color, lw=0.5)
axes[1].axis("off")
axes[1].set_title("Coronal Slice")
axes[1].axis("equal")
# Plot Sagittal slice
for streamline, color in zip(streamlines_to_display, colors):
axes[2].plot(streamline[:, 1], streamline[:, 2], color=color, lw=0.5)
axes[2].axis("off")
axes[2].set_title("Sagittal Slice")
axes[2].axis("equal")
plt.tight_layout()
plt.show()
DIPY#
streamlines_actor = actor.line(streamlines, colors=cmap.line_colors(streamlines))
scene = window.Scene()
scene.add(streamlines_actor)
window.record(scene=scene, out_path="streamlines.png", size=(600, 600))
Image("streamlines.png")
ipyniivue#
To help interpret the tractography, we also display the GM/WM interface used as the seeding region. Showing this surface makes it clear where the streamlines originate and how ACT constrains their trajectories.
# Create a new NiiVue viewer with a black background
nv3 = NiiVue(back_color=[0, 0, 0, 1])
nv3.opts.is_alpha_clip_dark = True
# Load the seed image (GM/WM interface) as a semi-transparent grayscale volume
nv3.load_volumes([
{"url": "https://huggingface.co/datasets/neurodeskorg/neurodeskedu/resolve/main/data/examples/diffusion_imaging/MRtrix_3/gmwmSeed_coreg_746969289733.mif", "colormap": "gray", "opacity": 0.5}
])
# Load the tractography streamlines as a mesh overlay
nv3.load_meshes([
{"url": "https://huggingface.co/datasets/neurodeskorg/neurodeskedu/resolve/main/data/examples/diffusion_imaging/MRtrix_3/tracks_10k_bb88bfc1773b.tck"}
])
nv3
Remember to inspect this image to make sure that the streamlines end where you think they should; in other words, the streamlines should be constrained to the white matter, and they should be color-coded appropriately. For example, the corpus callosum should be mostly red, and the corona radiata should be mostly blue.
Although we have created a diffusion image with reasonable streamlines, also known as a tractogram, we still have a problem with some of the white matter tracts being over-fitted, and others being under-fitted. This can be addressed with the tcksift2 command.
Refining the Streamlines with tcksift2#
You may ask why there is any need to modify the streamlines further once we have created our tractogram. The reason is that some tracts will be threaded with more streamlines than others, because the fiber orientation densities are much clearer and more attractive candidates for the probabilistic sampling algorithm that was discussed above. In other words, certain tracts can be over-represented by the amount of streamlines that pass through them not necessarily because they contain more fibers, but because the fibers tend to all be orientated in the same direction.
To counter-balance this overfitting, the command tcksift2 will create a text file containing weights for each voxel in the brain:
! tcksift2 -act ./MRtrix_fod/5tt_coreg.mif -out_mu ./MRtrix_fod/sift_mu.txt -out_coeffs ./MRtrix_fod/sift_coeffs.txt -nthreads 20 ./MRtrix_fod/tracks_1M.tck ./MRtrix_fod/wmfod_norm.mif ./MRtrix_fod/sift_1M.txt -force
tcksift2: [WARNING] existing output files will be overwritten
tcksift2: [ 9%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 13%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 17%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 20%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 22%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 24%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 25%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 27%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 29%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 30%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 31%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 32%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 34%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 35%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 36%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 37%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 38%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 39%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 40%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 42%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 43%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 44%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 46%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 47%] resampling ACT 5TT image to fixel image space...?7h
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tcksift2: [ 49%] resampling ACT 5TT image to fixel image space...?7h
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tcksift2: [ 52%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 54%] resampling ACT 5TT image to fixel image space...?7h
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tcksift2: [ 57%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 59%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 60%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 62%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 64%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 66%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 67%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 70%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 72%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 75%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 77%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 80%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 83%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [ 89%] resampling ACT 5TT image to fixel image space...?7h
tcksift2: [100%] resampling ACT 5TT image to fixel image space
tcksift2: [ 13%] segmenting FODs...?7h
tcksift2: [ 17%] segmenting FODs...?7h
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tcksift2: Iteration CF (data) CF (reg) Streamlines
.
.
tcksift2: [ . ] 1 37.888% 0.635% 999821?7h
.
.
tcksift2: [ . ] 2 28.729% 1.461% 999821?7h
.
.
tcksift2: [ .] 3 25.467% 2.165% 999821?7h
.
.
tcksift2: [ . ] 4 23.731% 2.702% 999821?7h
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.
tcksift2: [. ] 5 22.662% 3.103% 999821?7h
.
.
tcksift2: [. ] 6 21.944% 3.405% 999821?7h
.
.
tcksift2: [ . ] 7 21.432% 3.636% 999821?7h
.
.
tcksift2: [ .] 8 21.051% 3.817% 999821?7h
.
.
tcksift2: [ . ] 9 20.756% 3.963% 999821?7h
.
.
tcksift2: [ . ] 10 20.521% 4.083% 999821?7h
.
.
tcksift2: [. ] 11 20.329% 4.183% 999821?7h
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.
tcksift2: [ . ] 12 20.170% 4.268% 999821?7h
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.
tcksift2: [ . ] 13 20.036% 4.341% 999821?7h
.
.
tcksift2: [ .] 14 19.921% 4.405% 999821?7h
.
.
tcksift2: [ . ] 15 19.822% 4.461% 999821?7h
.
.
tcksift2: [ . ] 16 19.735% 4.511% 999821?7h
.
.
tcksift2: [ . ] 17 19.659% 4.556% 999821?7h
.
.
tcksift2: [ . ] 18 19.591% 4.596% 999821?7h
.
.
tcksift2: [ . ] 19 19.531% 4.632% 999821?7h
.
.
tcksift2: [ . ] 20 19.477% 4.665% 999821?7h
.
.
tcksift2: [ . ] 21 19.428% 4.695% 999821?7h
.
.
tcksift2: [ . ] 22 19.384% 4.723% 999821?7h
.
.
tcksift2: [ . ] 23 19.344% 4.748% 999821?7h
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.
tcksift2: [ . ] 24 19.308% 4.771% 999821?7h
.
.
tcksift2: [ . ] 25 19.274% 4.793% 999821?7h
.
.
tcksift2: [ . ] 26 19.244% 4.812% 999821?7h
.
.
tcksift2: [ .] 27 19.215% 4.831% 999821?7h
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.
tcksift2: [ .] 28 19.189% 4.848% 999821?7h
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.
tcksift2: [ . ] 29 19.165% 4.864% 999821?7h
.
.
tcksift2: [. ] 30 19.143% 4.879% 999821?7h
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.
tcksift2: [ . ] 31 19.122% 4.893% 999821?7h
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.
tcksift2: [ . ] 32 19.103% 4.906% 999821?7h
.
.
tcksift2: [. ] 33 19.085% 4.918% 999821?7h
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.
tcksift2: [ . ] 34 19.068% 4.930% 999821?7h
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.
tcksift2: [ .] 35 19.052% 4.941% 999821?7h
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.
tcksift2: [ . ] 36 19.038% 4.951% 999821?7h
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.
tcksift2: [. ] 37 19.024% 4.961% 999821?7h
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.
tcksift2: [ . ] 38 19.011% 4.970% 999821?7h
.
.
tcksift2: [ . ] 39 18.999% 4.979% 999821?7h
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.
tcksift2: [ . ] 40 18.987% 4.987% 999821?7h
.
.
tcksift2: [ . ] 41 18.976% 4.995% 999821?7h
.
.
tcksift2: [ . ] 42 18.966% 5.002% 999821?7h
.
.
tcksift2: [ . ] 43 18.956% 5.009% 999821?7h
.
.
tcksift2: [done] 44 18.947% 5.016% 999821
#
The output from the command, “sift_1M.txt”, can be used with the command tck2connectome to create a matrix of how much each ROI is connected with every other ROI in the brain - a figure known as a connectome - which will weight each ROI. To see how to do that, read the chapter on Creating and Viewing the Connectome of Andy’s Brain Book.
Dependencies in Jupyter/Python#
Using the package watermark to document system environment and software versions used in this notebook, alongside the Neurodesktop version extracted from the
JUPYTER_IMAGEorNEURODESKTOP_VERSIONenvironment variables.
import os
%load_ext watermark
%watermark
%watermark --iversions
neurodesktop_version = (
os.environ.get('JUPYTER_IMAGE', '').split(':')[-1] or
os.environ.get('NEURODESKTOP_VERSION', 'unknown')
)
print(f"Neurodesktop version: {neurodesktop_version}")
Last updated: 2026-04-09T02:43:12.503372+00:00
Python implementation: CPython
Python version : 3.13.9
IPython version : 9.7.0
Compiler : GCC 14.3.0
OS : Linux
Release : 5.15.0-171-generic
Machine : x86_64
Processor : x86_64
CPU cores : 32
Architecture: 64bit
IPython : 9.7.0
fury : 0.11.0
ipyniivue : 2.4.4
ipywidgets: 7.8.5
matplotlib: 3.10.8
nibabel : 5.3.3
numpy : 2.3.5
Neurodesktop version: 2025-12-20