Synopsis

Accurate segmentation of the thalamus and its nuclei is a prerequisite for studying anatomical connectivity and its correlation to neurological diseases. The probabilistic tractography pipeline in FSL is commonly used for thalamus connectivity-based parcellation. However, dMRI data analysis and tractography are done in a mix of standard and subject spaces which can bias anatomical connectivity findings. Here, we presented a framework that improves thalamus parcellation by performing DW data processing and probabilistic tractography in the subject’s native space, as well by generating population- connectivity priors. Higher segmentation accuracy was achieved with it when compared to FSL’s available pipeline.

Introduction

Methods

Data processing

Diffusion-weighted imaging (DW) was performed on 15 healthy individuals. Data was acquired twice along 64 non-collinear isotropic directions (b-value=1000 s/mm², 2.5 mm³ vox) and four additional b-0 volumes. Volumetric T1-weighted MPRAGE images were also acquired (1.1 mm³ vox).

DW data was corrected for motion, eddy-current and susceptibility artefacts using NiftTK [3], while T1- weighted images underwent bias correction and brain parcellation into 143 regions [4]. T1- weighted data, thalamus and cortical segmentations were propagated to the DW space through intra-subject registration.

Tractography

Multi-fibre orientations were inferred through constrained spherical deconvolution (CSD) [6]. Then, probabilistic tracking was done from the thalamus (seed) to each cortical region (target) using iFOD2 [7] (l_max=8 and total_tracts = 5000). Probability maps were derived for each target region’s connection likelihood.

Proposed segmentation framework

The iterative method (Fig. 1) starts off with initial estimates of probabilistic connectivity maps obtained through tractography. In order to introduce group consistency, a group average space was built through multichannel registration of T1 and FA maps of all normal controls using a cubic B-splines algorithm [5]. Secondly, connection maps from each subject and cortical region were mapped to the group space and averaged. The generated population-based probabilistic connection maps were propagated back to the individual’s space and considered as priors when inferring the new connection probability between every thalamic voxel to a set of cortical regions.

Thalamus parcellation

The maximum a posteriori estimates for each region using FSL and the proposed pipeline (PP) were used to classify thalamic voxels. The segmentation performance of both methods were evaluated by estimating the volume fractions of each connectivity-defined regions (CDRs). Underlying group dispersion was quantified through coefficients of variation (CV). Reproducibility of the PP was also tested by replicating the previous steps in two repeated scans, with subsequent comparison of CDRs volumes normalised by the total intracranial volume (TIV), and Dice score between test-retest thalamus parcellations.

Results

Example thalamus parcellations are shown in Fig. 2. At each iteration, the PP generates smoother probabilistic connectivity maps that led to more anatomically plausible segmentations.

The results generated by PP were compared to FSL. Table 1 describes the relative volumes of each CDR in FSL and proposed strategies. A strong and positive correlation was found between them for both hemispheres (left hemisphere r=0.98, P=0.001; right hemisphere r=0.93, P=0.007). Additionally, CVs from FSL were found to be pronouncedly higher than for the PP, showing a greater dispersion in the normalised volumes across healthy subjects (see Fig.3, where PP provides sharper parcellations).

Under the assumption of left/right symmetry in healthy thalami, the root-mean squared left/right volume differences (RMSD) were measured for each CDR. In this context, a lower RMSD can be seen as an indicator of better left/right symmetry and robustness of the segmentation method (Table 2). In agreement with better appearing parcellations, the PP also led to a lower variability of hemispheric volumes for each thalamus CDR, around 50% less than differences detected in FSL. Test-retest results for the PP are shown in Table 2, with reproducibility being measured by the correlation between test-retest normalised CDRs volumes and test-retest Dice overlap on thalamus parcellations.

Discussion

The thalamus clusters (Figs. 2 and 3) demonstrated good correspondence across subjects and with previous studies [8,9].

Anterior clusters were preferentially connected with prefrontal, motor and sensory cortices, while posterior clusters had dominant projections towards parietal, occipital and temporal areas. The proposed method showed greater consistency in anterior than in posterior CDRs (Table 2), likely due to well-known crossing neural pathways. Note that the increased anatomical complexity of the posterior CDRs made the proposed improvements more evident in this region.

Further improvements to the method will involve better acquisition, combining more information when building the group space, as well as a greater number of subjects and pathologies.

Acknowledgements

References