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Merged Group Tractography Evaluation with Selective Automated Group Integrated Tractography
David Q Chen1, Jidan Zhong2, David J Hayes2
1Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto ON, Canada.
Frontiers in Neuroanatomy
|October 30, 2016
Summary
Selective Automated Group Integrated Tractography (SAGIT) enables group-wise diffusion MRI analysis. Different tractography algorithms show varying strengths for neuroanatomy delineation, with SAGIT validating performance.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Medical Image Analysis
Background:
- Group-based tractography analysis using diffusion MRI (dMRI) is challenging across large populations.
- Existing methods lack accessibility and standardized group-level evaluation.
- Need for automated tools to facilitate large-scale dMRI studies.
Purpose of the Study:
- Introduce Selective Automated Group Integrated Tractography (SAGIT), a novel automated software platform for group-wise dMRI analysis.
- Evaluate the performance of different tractography algorithms (CST, XST, DTT) using SAGIT.
- Introduce and validate a Normalized Overlap Score (NOS) for assessing group tractography quality.
Main Methods:
- Acquired dMRI data from 42 healthy adults.
- Utilized Automated Normalization Tools for registration and FreeSurfer for segmentation.
- Applied four tractography algorithms (CST, XST, DTT) to delineate six neuroanatomical structures, with and without ROI filters.
- Evaluated tractography results using a Normalized Overlap Score (NOS) and expert neuroscientist ratings.
Main Results:
- Merged tractography within SAGIT revealed distinct fiber distribution characteristics for each algorithm.
- Constrained Spherical Deconvolution (CST) variants showed different sensitivities to false positives and anatomical complexity.
- Diffusion Tensor Tractography (DTT) exhibited the lowest reproducibility.
- Normalized Overlap Score (NOS) significantly correlated with rater scores for filtered tractography, validating its utility.
Conclusions:
- SAGIT provides reliable and consistent group-wise tractography analysis across multiple subjects and techniques.
- Quantifiably demonstrated algorithm-specific strengths and weaknesses for group-level neuroanatomy delineation.
- Suggests a hybrid approach using multiple algorithms for different anatomical segments is optimal for comprehensive analysis.
