Tractography
Last updated on 2024-02-18 | Edit this page
Overview
Questions
- What information can dMRI provide at the long range level?
Objectives
- Present different long range orientation reconstruction methods
Tractography
The local fiber orientation reconstruction can be used to map the voxel-wise fiber orientations to white matter long range structural connectivity. Tractography is a fiber tracking technique that studies how the local orientations can be integrated to provide an estimation of the white matter fibers connecting structurally two regions in the white matter.
Tractography models axonal trajectories as geometrical entities called streamlines from local directional information. Tractograhy essentially uses an integral equation involving a set of discrete local directions to numerically find the curve (i.e. the streamline) that joins them. The streamlines generated by a tractography method and the required meta-data are usually saved into files called tractograms.
The following is a list of the main families of tractography methods in chronological order:
- Local tractography (Conturo et al. 1999, Mori et al. 1999, Basser et al. 2000).
- Global tracking (Mangin et al. 2002)
- Particle Filtering Tractography (PFT) (Girard et al. 2014)
- Parallel Transport Tractography (PTT) (Aydogan et al., 2019)
Local tractography methods and PFT can use two approaches to propagate the streamlines:
- Deterministic: propagates streamlines consistently using the same propagation direction.
- Probabilistic: uses a distribution function to sample from in order to decide on the next propagation direction at each step.
Several algorithms exist to perform local tracking, depending on the local orientation construct used or the order of the integration being performed, among others: FACT (Mori et al. 1999), EuDX (Garyfallidis 2012), iFOD1 (Tournier et al. 2012) / iFOD2 (Tournier et al. 2010), and SD_STREAM (Tournier et al. 2012) are some of those. Different strategies to reduce the uncertainty (or missed configurations) on the tracking results have also been proposed (e.g. Ensemble Tractography (Takemura et al. 2016), Bootstrap Tractography (Lazar et al. 2005)).
Tractography methods suffer from a number of known biases and limitations, generally yielding tractograms containing a large number of prematurely stopped streamlines and invalid connections, among others. This results in a hard trade-off between sensitivity and specificity (usually measured in the form of bundle overlap and overreach) (Maier-Hein et al. 2017).
Several enhancements to the above frameworks have been proposed, usually based on incorporating some a priori knowledge (e.g. Anatomically-Constrained Tractography (ACT) (Smith et al. 2012), Structure Tensor Informed Fiber Tractography (STIFT) (Kleinnijenhuis et al. 2012), Surface-enhanced Tractography (SET) (St-Onge et al. 2018), Bundle-Specific Tractography (BST) (Rheault et al., 2019), etc.).
In the recent years, many deep learning methods have been proposed to map the local orientation reconstruction (or directly the diffusion MRI data) to long range white matter connectivity.