Global tractography is the process of finding the full track configuration that best explains the measured DWI data. As opposed to streamline tracking, global tractography is less sensitive to noise, and the density of the resulting tractogram is directly related to the data at hand.
As of version 3.0, MRtrix supports global tractography using a multi-tissue spherical convolution model, as introduced in Christiaens et al. (2015). This method extends the method of Reisert et al. (2011) to multi-shell response functions, estimated from the data, and adopts the multi-tissue model presented in Jeurissen et al. (2014) to account for partial voluming.
The most common use will be:
tckglobal dwi.mif wmr.txt -riso csfr.txt -riso gmr.txt -mask mask.mif -niter 1e8 -fod fod.mif -fiso fiso.mif tracks.tck
In this example,
dwi.mif is the input dataset, including the
gradient table, and
tracks.tck is the output tractogram.
Input response functions¶
Input response functions for (single fibre) white matter, grey matter, and CSF can be estimated from the data in prior tissue segmentations, as described in Jeurissen et al. (2014) and Christiaens et al. (2015).
Obtaining good segmentations of WM, GM and CSF will typically require T1 data. While MRtrix doesn’t implement segmentation methods itself, it does provide a script that calls the relevant FSL or Freesurfer tools to obtain a tissue segmentation in the appropriate format, for example:
5ttgen fsl T1.mif 5tt.mif
Note that the T1 image must be aligned with (e.g. registered to) the DWI data. See this page for more information.
Response functions for single-fibre WM, GM, and CSF, can then be estimated using:
dwi2response msmt_5tt dwi.mif 5tt.mif wm.txt gm.txt csf.txt
For a detailed explanation of different strategies for response function estimation, have a look at this page.
-niter: The number of iterations in the optimization. Although the
default value is deliberately kept low, a full brain reconstruction will
require at least 100 million iterations.
-lmax: Maximal order of the spherical harmonics basis.
-length: Length of each track segment (particle), which determines
the resolution of the reconstruction.
-weight: Weight of each particle. Decreasing its value by a factor
of two will roughly double the number of reconstructed tracks, albeit at
increased computation time.
-ppot: The particle potential essentially
associates a cost to each particle, relative to its weight. As such,
we are in fact trying to reconstruct the data as good as possible, with
as little particles as needed. This ensures that there is sufficient
proof for each individual particle, and hence avoids that a bit of
noise in the data spurs generation of new (random) particles. Think of
it as a parameter that balances sensitivity versus specificity. A higher
particle potential requires more proof in the data and therefore leads
to higher specificity; a smaller value increases sensitivity.
-cpot: The connection potential is the driving
force for connecting segments and hence building tracks. Higher values
increase connectivity, at the cost of increased invalid connections.
-fod: Outputs the fODF as an image of spherical harmonics
coefficients. The fODF is obtained by adding apodised PSFs along the
directions of all segments in a voxel, akin to track orientation
distribution imaging (TODI, Dhollander et al., 2014).
However, as global tractography matches the track density to the
underlying data, the distinction between both is mute.
-fiso: Outputs the estimated density of all isotropic tissue
components, as multiple volumes in one 4-D image in the same order as
-riso kernels were provided.
-eext: Outputs the residual data energy image, including the
L1-penalty imposed by the particle potential.
- D. Christiaens, M. Reisert, T. Dhollander, S. Sunaert, P. Suetens, and F. Maes. Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model. NeuroImage, 123 (2015) pp. 89–101 [SD link]
- M. Reisert, I. Mader, C. Anastasopoulos, M. Weigel, S. Schnell, and V. Kiselev. Global fiber reconstruction becomes practical. NeuroImage, 54 (2011) pp. 955–962 [SD link]
- B. Jeurissen, J.D. Tournier, T. Dhollander, A. Connelly, and J. Sijbers. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. NeuroImage, 103 (2014), pp. 411–426 [SD link]
- T. Dhollander, L. Emsell, W. Van Hecke, F. Maes, S. Sunaert, and P. Suetens. Track Orientation Density Imaging (TODI) and Track Orientation Distribution (TOD) based tractography. NeuroImage, 94 (2014), pp. 312–336 [SD link]