# A Comparison of Skeletal Muscle Diffusion Tensor Imaging Tractography Seeding Methods

## TL;DR

This paper compares different methods for seeding fiber tracts in muscle imaging to determine which best captures muscle architecture for understanding muscle function.

## Contribution

The study introduces and evaluates updated aponeurosis seeding and voxel-based seeding methods for DTI tractography in skeletal muscle.

## Key findings

- Updated aponeurosis seeding improves accuracy and robustness of tract propagation.
- Voxel-based seeding produces comparable results to aponeurosis seeding while accelerating workflow.
- Voxel-based methods may be more suitable for high-throughput analysis of multiple muscles.

## Abstract

The internal arrangement of a muscle's fibers with respect to its mechanical line of action (muscle architecture) is a major determinant of muscle function. Muscle architecture can be quantified using diffusion tensor magnetic resonance imaging‐based tractography, which propagates streamlines from a set of seed points by integrating vectors that represent the direction of greatest water diffusion (and by inference, the local fiber orientation). Previous work in skeletal muscle has demonstrated that tractography outcomes are sensitive to the method for defining seed points, but this sensitivity has not been fully examined. To do so, we developed a realistic simulated muscle architecture and implemented three methods for tract seeding: seeding along the muscle‐aponeurosis boundary with an updated procedure for rounding seed points prior to lookup in the muscle boundary mask and diffusion tensor matrices (APO); voxel‐based seeding throughout the muscle volume at a uniform spatial frequency (VXL); and seeding near external and internal muscle boundaries (EDGE). We then implemented these methods in example human datasets. The updated aponeurosis seeding procedures allow more accurate and robust tract propagation from seed points. The voxel‐based seeding methods had quantification outcomes that closely matched the updated aponeurosis seeding method. Further, the voxel‐based methods can accelerate the overall workflow and may be beneficial in high throughput analysis of multi‐muscle datasets. Continued evaluation of these methods in a wider range of muscle architectures is warranted.

Diffusion‐tensor imaging (DTI) fiber tracking can be used to quantify skeletal muscle architecture, allowing studies of muscle structure‐function relationships. However, the options for defining the seed points (starting points for integrating the first eigenvector of the diffusion tensor) to form the fiber tracts have not been evaluated. We used simulation and experimental approaches to evaluate the impact of three seeding strategies on the muscle architecture estimates and the uniformity with which the fiber tracts sample the muscle volume.