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Section: New Results
From dMRI to Fiber Pathways
Towards quantitative connectivity analysis: reducing tractography biases
Participants : Gabriel Girard [SCIL Lab., Sherbrooke University] , Kevin Whittingstall [SCIL Lab., Sherbrooke University] , Maxime Descoteaux [SCIL Lab., Sherbrooke University] , Rachid Deriche.
Diffusion MRI tractography is often used to estimate structural connections between brain areas and there is a fast-growing interest in quantifying these connections based on their position, shape, size and length. However, a portion of the connections reconstructed with tractography is biased by their position, shape, size and length. Thus, connections reconstructed are not equally distributed in all white matter bundles. Quantitative measures of connectivity based on the streamline distribution in the brain such as streamline count (density), average length and spatial extent (volume) are biased by erroneous streamlines produced by tractography algorithms. In this work, solutions are proposed to reduce biases in the streamline distribution. First, we propose to optimize tractography parameters in terms of connectivity. Then, we propose to relax the tractography stopping criterion with a novel probabilistic stopping criterion and a particle filtering method, both based on tissue partial volume estimation maps calculated from a T1-weighted image. We show that optimizing tractography parameters, stopping and seeding strategies can reduce the biases in position, shape, size and length of the streamline distribution. These tractography biases are quantitatively reported using in-vivo and synthetic data. This is a critical step towards producing tractography results for quantitative structural connectivity analysis.
This work has been published in [13]
Choosing tractography parameters to improve connectivity mapping
Participants : Gabriel Girard [SCIL Lab., Sherbrooke University] , Kevin Whittingstall [SCIL Lab., Sherbrooke University] , Maxime Descoteaux [SCIL Lab., Sherbrooke University] , Rachid Deriche.
Diffusion-weighted imaging (DWI) is often used as a starting point for in vivo white matter (WM) connectivity to reconstruct potential WM pathways between brain areas. Tractography algorithms have many parameters which can influence reconstruction and connectivity. Various choices of parameters have been proposed. But how to choose the best set of parameters ? In this study, we varied three critical parameters while monitoring connectivity score using the Tractometer evaluation system on the International Symposium on Biomedical Imaging (ISBI) Challenge synthetic dataset. The three parameters were: The maximum deviation angle between two consecutive tractography steps (this addresses the hypothesis of smoothness of the WM pathways), the spherical function (SF) threshold (this aims at removing noisy propagation directions during the tractography process) and the initial SF threshold (this aims at removing initial noise at the seeds and to start tractography in a good tangent direction to the WM bundle).
This work has been published in [20]
Connectivity directionally-encoded color map: a streamline-based color mapping
Participants : Gabriel Girard [SCIL Lab., Sherbrooke University] , Kevin Whittingstall [SCIL Lab., Sherbrooke University] , Maxime Descoteaux [SCIL Lab., Sherbrooke University] , Rachid Deriche.
In this work, we provide a novel method to map streamlines in a color image, which can be generated from any set of streamlines. We show that this novel orientation color-coded map based on streamline tractography can improve connectivity analysis.
This work has been published in [23]