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A Comparison of Skeletal Muscle Diffusion Tensor Imaging Tractography Seeding Methods.
Bruce M Damon1,2,3,4,5, Roberto Pineda Guzman1, Carly A Lockard1
1Carle Clinical Imaging Research Program, Stephens Family Clinical Research Institute, Carle Health, Urbana IL USA 61801.
Biorxiv : the Preprint Server for Biology
|September 11, 2024
Summary
New seeding methods for diffusion tensor imaging-based tractography improve muscle architecture quantification. Voxel-based approaches offer accuracy and efficiency for analyzing muscle fiber orientation.
Area of Science:
- Biomechanics
- Medical Imaging
- Neuroscience
Background:
- Muscle architecture significantly influences muscle function.
- Diffusion tensor imaging (DTI)-based tractography quantifies muscle architecture by tracking water diffusion.
- Tractography results are sensitive to seed point selection, necessitating method evaluation.
Purpose of the Study:
- To develop and evaluate novel seed point selection methods for DTI-based muscle tractography.
- To compare the accuracy and efficiency of different tract seeding strategies.
Main Methods:
- Developed a simulated muscle architecture and implemented four seeding methods: APO-3 (aponeurosis boundary), VXL-1 (uniform voxel), VXL-2 (variable voxel), and VXL-3 (near boundaries).
- Applied these methods to a human muscle dataset.
- Assessed tract propagation accuracy and workflow efficiency.
Main Results:
- Updated aponeurosis seeding (APO-3) enhanced tract propagation accuracy and robustness.
- Voxel-based methods (VXL-1, VXL-2, VXL-3) yielded quantification outcomes comparable to APO-3.
- Voxel-based methods demonstrated potential for accelerating high-throughput analysis.
Conclusions:
- Novel voxel-based seeding methods provide accurate and efficient alternatives for DTI-based muscle architecture analysis.
- These methods may improve the analysis of large multi-muscle datasets.
- Further validation across diverse muscle architectures is recommended.
