Frequently Asked Questions (FAQ)

Processing of HCP data

We expect that a number of users will be wanting to use MRtrix3 for the analysis of data from the Human Connectome Project (HCP). These data do however present some interesting challenges from a processing perspective. Here I will try to list a few ideas, as well as issues that do not yet have a robust solution; I hope that any users out there with experience with these data will also be able to contribute with ideas or suggestions.

Do my tracking parameters need to be changed for HCP data?

Probably. For instance, the default parameters for length criteria are currently set based on the voxel size rather than absolute values (so e.g. animal data will still get sensible defaults). With such high resolution data, these may not be appropriate. The default maximum length is 100 times the voxel size, or only 125mm at 1.25mm isotropic; this would preclude reconstruction of a number of long-range pathways in the brain, so should be overridden with something more sensible. The minimum length is more difficult, but in the absence of a better argument I’d probably stick with the default (5 x voxel size, or 2 x voxel size if ACT is used).

Also, the default step size for iFOD2 is 0.5 times the voxel size; this will make the track files slightly larger than normal, and will also make the tracks slightly more jittery, but actually disperse slightly less over distance, than standard resolution data. People are free to experiment with the relevant tracking parameters, but we don’t yet have an answer for how these things should ideally behave.

Is it possible to use data from all shells in CSD?

The default CSD algorithm provided in the dwi2fod command is only compatible with a single b-value shell, and will by default select the shell with the largest b-value for processing.

The Multi-Shell Multi-Tissue (MSMT) CSD method has now been incorporated into MRtrix3, and is provided as part of the dwi2fod command. There are also instructions for its use provided in the documentation.

The image data include information on gradient non-linearities. Can I make use of this?

Again, unfortunately not yet. Making CSD compatible with such data is more difficult than other diffusion models, due to the canonical response function assumption. To me, there are two possible ways that this could be handled:

  • Use the acquired diffusion data to interpolate / extrapolate predicted data on a fixed b-value shell.
  • Generate a representation of the response function that can be interpolated / extrapolated as a function of b-value, and therefore choose an appropriate response function per voxel.

Work is underway to solve these issues, but there’s nothing available yet. For those wanting to pursue their own solution, bear in mind that the gradient non-linearities will affect both the effective b-value and the effective diffusion sensitisation directions in each voxel. Otherwise, the FODs look entirely reasonable without these corrections...

The anatomical tissue segmentation for ACT from 5ttgen fsl seems even worse than for ‘normal’ data...?

The combination of high spatial resolution and high receiver coil density results in a pretty high noise level in the middle of the brain. This in turn can trick an intensity-based segmentation like FSL’s FAST into mislabeling things; it just doesn’t have the prior information necessary to disentangle what’s in there. I haven’t looked into this in great detail, but I would very much like to hear if users have discovered more optimal parameters for FAST, or alternative segmentation software, for which they have been impressed by the results.

Generating Track-weighted Functional Connectivity (TW-FC) maps

This example demonstrates how these maps were derived, precisely as performed in the relevant NeuroImage paper. Assumes that you have a whole-brain tractogram named tracks.tck, and a 3D volume named FC_map.mif representing an extracted FC map with appropriate thresholding.

Initial TWI generation:

$ tckmap tracks.tck temp.mif <-template / -vox options> -contrast scalar_map -image FC_map.mif -stat_vox mean -stat_tck sum

Deriving the mask (voxels with at least 5 streamlines with non-zero TW values):

$ tckmap tracks.tck - -template temp.mif -contrast scalar_map_count -image FC_map.mif | mrcalc - 5 -ge mask.mif -datatype bit

Apply the mask:

$ mrcalc temp.mif mask.mif -mult TWFC.mif

Handling SIFT2 weights

With the original tcksift command, the output is a new track file, which can subsequently be used as input to any command independently of the fact that SIFT has been applied. SIFT2 is a little trickier: the output of the tcksift2 command is a text file. This text file contains one line for every streamline, and each line contains a number; these are the weights of the individual streamlines. Importantly, the track file that was used as input to the tcksift2 command is unaffected by the execution of that command.

There are therefore two important questions to arise from this:

How do I use the output from SIFT2?

Any MRtrix3 command that receives a track file as input will also have a command-line option, -tck_weights_in. This option is used to pass the weights text file to the command. If this option is omitted, then processing will proceed as normal for the input track file, but without taking the weights into consideration.

Why not just add the weight information to the track data?

The .tck file format was developed quite a long time ago, and doesn’t have the capability of storing such data. Therefore, combining per-streamline weighting data with the track data itself would require either modifying this format (which would break compatibility with MRtrix 0.2, and any other non-MRtrix code that uses this format), using some other existing format for track data (which, given our experiences with image formats, can be ill-devised), or creating a new format (which would need to support a lot more than just per-streamline weights in order to justify the effort, and would likely become a fairly lengthy endeavour).

Furthermore, writing to such a format would require duplicating all of the raw track data from the input file into a new output file. This is expensive in terms of time and HDD space; the original file could be deleted afterwards, but it would then be difficult to perform any operations on the track data where the streamline weight information should be ignored (sure, you could have a command-line option to ignore the weights, but is that any better than having a command-line option to input the weights?)

So, for now, it is best to think of the weights file provided by tcksift2 as accompanying the track file, containing additional data that must be explicitly provided to any commands in order to be used. The track file can also be used without taking into account the streamline weights, simply by not providing the weights.

Making use of Python scripts library

In addition to the principal binary commands, MRtrix3 also includes a number of Pyton scripts for performing common image processing tasks. These make use of a relatively simple set of library functions that provide a certain leven of convenience and consistency for building such scripts (e.g. common format help page; command-line parsing; creation, use and deletion of temporary working directory; control over command-line verbosity).

It is hoped that in addition to growing in complexity and capability over time, this library may also be of assistance to users when building their own processing scripts, rather than the use of e.g. Bash. The same syntax as that used in the provided scripts can be used. If however the user wishes to run a script that is based on this library, but is not located within the MRtrix3 scripts/ directory, it is necessary to explicitly inform Python of the location of those libraries; e.g.:

$ export PYTHONPATH=/home/user/mrtrix3/lib:$PYTHONPATH
$ ./my_script [arguments] (options)

(Replace the path to the MRtrix3 “lib” directory with the location of your own installation)

tck2connectome no longer has the -contrast X option...?

The functionalities previously provided by the -contrast option in this command can still be achieved, but through more explicit steps:

tck2connectome -contrast mean_scalar

$ tcksample tracks.tck scalar.mif mean_scalars.csv -stat_tck mean
$ tck2connectome tracks.tck nodes.mif connectome.csv -scale_file mean_scalars.csv -stat_edge mean

The first step samples the image scalar.mif along each streamline, calculates the mean sampled value along each streamline, and stores these values into file mean_scalars.csv (one value for every streamline). The second step then assigns the value associated with each streamline during connectome construction to be the values from this file, and finally calculates the value of each edge to be the mean of the values for the streamlines in that edge.

tck2connectome -contrast meanlength

$ tck2connectome tracks.tck nodes.mif connectome.csv -scale_length -stat_edge mean

For each streamline, the contribution of that streamline to the relevant edge is scaled by the length of that streamline; so, in the absence of any other scaling, the contribution of that streamline will be equal to the length of the streamline in mm. Finally, for each edge, take the mean of the values contributed from all streamlines belonging to that edge.

tck2connectome -contrast invlength_invnodevolume

$ tck2connectome tracks.tck nodes.mif connectome.csv -scale_invlength -scale_invnodevol

Rather than acting as a single ‘contrast’, scaling the contribution of each streamline to the connectome by both the inverse of the streamline length and the inverse of the sum of node volumes is now handled using two separate command-line options. The behaviour is however identical to the old usage.

Visualising streamlines terminations

I am frequently asked about Figures 5-7 in the Anatomically-Constrained Tractography article, which demonstrate the effects that the ACT method has on the locations of streamlines terminations. There are two different techniques used in these figures, which I’ll explain here in full.

  • Figure 6 shows streamlines termination density maps: these are 3D maps where the intensity in each voxel reflects the number of streamlines terminations within that voxel. So they’re a bit like Track Density Images (TDIs), except that it’s only the streamlines termination points that contribute to the map, rather than the entire streamline. The easiest way to achieve this approach is with the tckmap command, using the -ends_only option.
  • Figures 5 and 7 display large dots at the streamline endpoints lying within the displayed slab, in conjunction with the streamlines themselves and a background image. Unfortunately this functionality is not yet implemented within MRtrix3, so duplicating this type of visualisation requires a bit of manual manipulation and software gymnastics:
    • Use the new tckresample command, with the -endpoints option, to generate a new track file that contains only the two endpoints of each streamline.
    • Load this track file into the old MRtrix 0.2 version of ``mrview``. This software can be acquired here. Note that you will likely want to not run the installation component of the build for this software; that way you should not encounter issues with conflicting commmand names between MRtrix versions. This does however mean that you will need to provide the full path to the MRtrix 0.2 mrview executable in order to run it.
    • Within the mrview tractography tool, enable the ‘depth blend’ option. This will display each streamline point as a dot, rather than drawing lines between the streamline points.
    • Adjust the brightness / contrast of the background image so that it is completely black.
    • Take a screenshot.
    • Remove the streamline endpoints track file from the tractography tool, and disable the ‘depth blend’ option (it’s best to disable the ‘depth blend’ option before opening any larger track file).
    • Reset the windowing of the main image, and/or load the complete tracks file, and take an additional screenshot, making sure not to move the view focus or resize the mrview window (so that the two screenshots overlay on top of one another).
    • The two screenshots are then combined using image editing software such as GIMP. The colors of the termination points can also be modified independently before they are combined with the second screenshot. One trick I used in this manuscript was to rotate the hue of the termination screenshot by 180 degrees: this provides a pseudo-random coloring of the termination points that contrasts well against the tracks.

Compiler error during build

If you encounter an error during the build process that resembles the following:

ERROR: (#/#) [CC] release/cmd/command.o

/usr/bin/g++-4.8 -c -std=c++11 -pthread -fPIC -I/home/user/mrtrix3/eigen -Wall -O2 -DNDEBUG -Isrc -Icmd -I./lib -Icmd cmd/command.cpp -o release/cmd/command.o

failed with output

g++-4.8: internal compiler error: Killed (program cc1plus)
Please submit a full bug report,
with preprocessed source if appropriate.
See for instructions.

This is most typically caused by the compiler running out of RAM. This can be solved either through installing more RAM into your system, or by restricting the number of threads to be used during compilation:


Hanging on network file system when writing images

When any MRtrix3 command must read or write image data, there are two primary mechanisms by which this is performed:

1. Memory mapping: The operating system provides access to the contents of the file as though it were simply a block of data in memory, without needing to explicitly load all of the image data into RAM.

2. Preload / delayed write-back: When opening an existing image, the entire image contents are loaded into a block of RAM. If an image is modified, or a new image created, this occurs entirely within RAM, with the image contents written to disk storage only at completion of the command.

This design ensures that loading images for processing is as fast as possible and does not incur unnecessary RAM requirements, and writing files to disk is as efficient as possible as all data is written as a single contiguous block.

Memory mapping will be used wherever possible. However one circumstance where this should not be used is when write access is required for the target file, and it is stored on a network file system: in this case, the command typically slows to a crawl (e.g. progressbar stays at 0% indefinitely), as the memory-mapping implementation repeatedly performs small data writes and attempts to keep the entire image data synchronised.

MRtrix3 will now test the type of file system that a target image is stored on; and if it is a network-based system, it will not use memory-mapping for images that may be written to. However, if you experience the aforementioned slowdown in such a circumstance, it is possible that the particular configuration you are using is not being correctly detected or identified. If you are unfortunate enough to encounter this issue, please report to the developers the hardware configuration and file system type in use.

Linux: very slow performance when writing large images

This might be due to the Linux Disk Caching or the kernel’s handling of dirty pages.

On Ubuntu, you can get your current dirty page handling settings with sysctl -a | grep dirty. Those settings can be modified in /etc/sysctl.conf by adding the following two lines to /etc/sysctl.conf:

vm.dirty_background_ratio = 60
vm.dirty_ratio = 80

vm.dirty_background_ratio is a percentage fraction of your RAM and should be larger than the image to be written. After changing /etc/sysctl.conf, execute sysctl -p to configure the new kernel parameters at runtime. Depending on your system, these changes might not be persistent after reboot.