Erick Hernandez-Gutierrez^{1}, Alonso Ramirez-Manzanares^{2}, Antoine Théberge^{1}, Maxime Descoteaux^{1}, Luis Concha^{3}, and Ricardo Coronado-Leija^{4}

^{1}Université de Sherbrooke, Sherbrooke, QC, Canada, ^{2}Centro de Investigación en Matemáticas A.C., Guanajuato, Mexico, ^{3}Universidad Nacional Autónoma de México, Querétaro, Mexico, ^{4}New York University School of Medicine, New York, NY, United States

In this study, we propose a pipeline to compute the structural connectome from diffusion-weighted magnetic resonance imaging (dMRI). The pipeline is based on a novel method called MRDS, which computes independent Gaussian profiles per intravoxel diffusion compartments. The effectiveness of our pipeline is shown on the DiSCo challenge synthetic dataset. Our findings show that the pipeline is competitive and outperforms a pipeline based on constrained spherical deconvolution (CSD).

The methodology is structured in 5 stages. The stages are described in sequential order as following:

$$ODF(n)=\sum_{i}^{N} \alpha_{i}\frac{ (n^{T} D_{i}^{-1} n )^{-3/2}}{4\pi\sqrt{ det(D_{i}) }}$$

where $$$D_i$$$ and $$$\alpha_{i}$$$ are the diffusion tensor and its compartment size associated with the i-th fiber bundle. On the other hand, for comparison we used the muti-shell multi-tissue version of the classic CSD method

- A home-made python implementation that removes streamlines not connecting 2 ROIs and streamlines connecting a ROI with itself.

- Finally, the resulting tractogram is given as input for COMMIT
^{3}, which aims to remove streamlines that do not reconstruct the diffusion weighted signal (mostly associated with false connections).

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**Fig.2** *Visual comparison of the streamlines connecting 2 grey colored ROIs. The leftmost tractogram represents the white colored GT strands trajectories. The one in the middle is the overlapped GT and CSD tractograms. The rightmost represents the overlapped GT and MRDS tractograms. This experiment shows the higher account of FC connections in the MRDS tractogram as well as the better overlapping with the GT.*

DOI: https://doi.org/10.58530/2022/3551