Tractometer: towards validation of tractography pipelines.

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Medical Image Analysis

Background:

• Tractography processing pipelines require robust evaluation and validation.
• Existing methods for assessing tractography outputs are limited.
• The development of standardized validation systems is crucial for advancing neuroimaging research.

Purpose of the Study:

• To introduce the Tractometer, an online system for evaluating and validating tractography processing pipelines.
• To provide a comprehensive platform for analyzing a large number of tractography outputs under diverse settings.
• To identify key factors influencing tractography quality and reliability.

Main Methods:

• Developed an online evaluation and validation system (Tractometer).
• Utilized a revised FiberCup analysis framework.
• Evaluated over 57,000 fiber tracking outputs across various acquisition settings, local estimation techniques, and tracking parameters.

Main Results:

• Averaging of diffusion MRI data demonstrably improves tractography quality.
• Sharp angular profiles in Orientation Distribution Functions (ODFs) positively impact tractography accuracy.
• Seeding and multi-seeding strategies significantly influence tractography outcomes and require careful application.
• Deterministic tractography generated fewer invalid tracts, leading to superior connectivity results compared to probabilistic tractography.

Conclusions:

• The Tractometer system offers a valuable tool for assessing tractography pipeline performance.
• Findings highlight the importance of data averaging, ODF sharpness, and seeding strategies in achieving reliable tractography.
• Deterministic tractography is recommended for improved connectivity analysis due to its higher accuracy and reduced errors.