A Comparison of Skeletal Muscle Diffusion Tensor Imaging Tractography Seeding Methods

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.

Keywords: DTI; fiber‐tracking; freeware; muscle architecture; simulation; skeletal muscle.

FIGURE 1

Composite tissue design and fiber orientations in the simulated muscle. (A–C) Angles of azimuth (ϕ), shown as counterclockwise rotations from the +X axis. (A) Axial view. The elliptical‐profiled tissue had a central aponeurosis in which the collagen orientation was perpendicular to the slice plane. White lines show the locations of the coronal (horizontal line) and sagittal (vertical line) views. The color bar gives ϕ in degrees. (B) Coronal view. The same color scale as in Panel (A) is used. White lines give the locations of the axial (horizontal line) and sagittal (vertical line) views. (C) Sagittal view. The angle of decreased as a function of increasing slice number. The same color scale as in Panel (A) is used. White lines give the locations of the axial (horizontal line) and coronal (vertical line) views. (D–F) Same as (A)–(C), except that the angle of elevation above the slice plane (θ) is shown. (D) Axial view. The color scale indicates the θ in degrees. θ increased as a function of within‐slice distance from the aponeurosis. (E–F) Coronal and sagittal views. The same color scale as (D) is shown. θ increased with increasing slice number.

FIGURE 2

Fiber‐tracts generated under noise‐free conditions (simulated muscle). (A–D) Noise‐free simulations, with (A) All fiber tracts, APO seeding; (B) FSS500, APO seeding; (C) FSS500, VXL seeding; and (D) FSS500, EDGE seeding. (E–G) SNR = 39 simulations, with FSS500 sampling for E. APO seeding; (F) VXL seeding; and (G) EDGE seeding. Seeding methods are indicated by color and with labels for each panel.

FIGURE 3

Uniformity of spatial sampling of the muscle by the fiber tracts: Effects of seeding method, noise, and sampling condition (simulated muscle). Uniformity is expressed as the variability in the average number of fiber tract points per voxel (Pts./Voxel) in each slice. (A) E. APO seeding, with (A) showing data from noise‐free images (all fibers); (B) showing data from noise‐free images (FSS1000); (C) showing data from noise‐free images (FSS500); (D) showing data from SNR = 54 and FSS500; and (E) showing data from SNR = 39 and FSS500. (F–J) Same as (A)–(E), but for VXL seeding. (K–O) Same as (A)–(E), but for EDGE seeding. All plots with SNR = 39 or 54 data show example results from the 1000th noise realization trial. Seeding methods are indicated by color and with Y‐axis labels. Note differences in Y‐axis scales.

FIGURE 4

Bland–Altman plots illustrating the agreement between α angles derived directly from ground‐truth values for ε₁ and the fiber‐tracts. (A–C) Noise‐free data, with ground truth angles and tract‐derived values. Panels (A)–(C) show APO, VXL, and EDGE seeding, respectively. (D–F) Same as (A)–(C), except for FSS500 and SNR = 54. (G–I) Same as (A)–(C), except for FSS500 and SNR = 39. Solid horizontal lines indicate the mean difference; dashed horizontal lines indicate the limits of agreement. In all plots, every 10th point is illustrated to improve clarity of presentation. All plots with SNR = 39 or 54 data show results from the 1000th noise realization trial. Seeding methods are indicated by color and with labels at the top of each column. Note differences in Y‐axis scales.

FIGURE 5

Fiber tracts from the in vivo studies. Panels (A)–(C) show fiber tracts generated using APO, VXL, and EDGE seeding, respectively, from the tibialis anterior muscle under the FSS500 sampling condition. Panels (D)–(F) show fiber tracts generated using APO, VXL, and EDGE seeding, respectively, from the gluteus maximus muscle under the FSS1000 sampling condition. Within each panel, the aspect ratio matches actual anatomical dimensions. Panels (A)–(C) have consistent scaling and panels (D)–(F) have consistent scaling. Seeding methods are indicated by color and with labels between the upper and lower rows.

FIGURE 6

Uniformity of spatial sampling of the muscle by the fiber tracts (in vivo data). Uniformity is expressed as the variability in the average number of fiber tract points per voxel in each slice. Panels (A)–(C) show data for the TA and panels (D)–(E) show data for the G_Max, with (A) and (D) showing data for APO, (B) and (E) showing data for VXL, and (C) and (F) showing data for EDGE. For the G_Max, data from every other slice are plotted to improve clarity. Seeding methods are indicated by color and with labels at the top of each column. Note differences in Y‐axis scales.