Multi-tensor fixel-based metrics in tractometry: application to multiple sclerosis.

Area of Science:

• Neuroimaging
• Diffusion MRI
• White Matter Tractography

Background:

• Traditional Diffusion Tensor Imaging (DTI) metrics struggle with crossing fibers and lesions.
• Single diffusion tensor models limit sensitivity and complicate interpretation in complex white matter regions.

Purpose of the Study:

• To develop an advanced tractometry pipeline utilizing multi-tensor fixel-based metrics.
• To improve the robustness and interpretability of white matter analysis in challenging neuroanatomical areas.

Main Methods:

• Multi-tensor metrics estimated using the Multi-Resolution Discrete Search (MRDS) method.
• Tractography-regularized spatial coherence of the multi-tensor field via Track Orientation Density Imaging.
• An end-to-end pipeline from raw data to track-specific multi-tensor metrics.

Main Results:

• The pipeline demonstrates superior performance over traditional single-tensor metrics in phantom and healthy cohort studies.
• Multi-tensor metrics show robustness to noise and accuracy in crossing fiber regions.
• Effective detection of white matter anomalies in relapsing-remitting multiple sclerosis patients.

Conclusions:

• The proposed multi-tensor tractometry pipeline offers enhanced sensitivity and interpretability.
• This approach is valuable for analyzing white matter integrity in the presence of complex pathologies like lesions and crossing fibers.