# Fatigue Effects on the Lower Leg Muscle Architecture Using Diffusion Tensor MRI
## 4 Discussion  
The aim of this study was to determine the architectural parameters of the human lower leg muscles (volume, ACSAmax, muscle length, pennation angle, PCSA and fiber length) using quantitative variables that were provided by diffusion tensor and anatomical MRI data. To accomplish this, the process started with the visualization and delimitation of ROIs corresponding to each muscle in each slice of a DP image scan. Afterward, the maximal ACSA and the length of the muscle were assessed, followed by a fiber-tracking algorithm that mapped the diffusion tensor, allowing for the reconstruction of the muscle fibers and subsequent estimation of fiber length, pennation angles and PCSA. For the second aim of this study, we ascertained whether short-term exhaustive stretch-shortening cycle exercise would induce changes in these muscle architecture parameters and whether DTI could be used to study acute muscular responses to exercise. The muscle volume did not change significantly after the acute exercise, although the increased water content in exercising muscles is known to affect both extra- and intracellular volumes [40,41]. One of the reasons for this unchanged result may have been the time between the end of the exercise and the duration of the scan protocol. Due to the rapid decay of the DTI signal, the DP scan was performed only after the DTI scan. It is possible that the recovery of muscles occurred rapidly and when drawing the muscles' boundaries to calculate the muscle volume, the anatomical images did not reveal significant  
Figure 4: Inter-subject variability regarding muscle shape. Measured values for the muscle volume and the product of ACSAmax and muscle length (**first line**), ACSAmax (**second line**) and muscle length (**third line**) before and after the exercise. The values for the CV are presented near the regression lines and also in Table 3.  
differences. Nevertheless, quantifying the muscle volume allowed us to quantify a subject-specific architectural parameter, which was needed to calculate an individual's PCSA. The manual segmentation of the muscles from the MRI images is an arduous process when automatic routines are not used. As a manual process, the delimitation of the muscle contours was subject to error, especially in the images where the muscle boundaries were not well defined. Nevertheless, that task was always performed by the same operator. Advanced post-processing approaches that are designed for musculoskeletal modeling will further improve the efficiency of extracting muscle anatomy from MR images. Muscle models generally assume that the pennation angle is constant across all fibers. In accordance with other imaging studies [29, 42], we verified that fibers from the same muscle had different pennations (and we used the mean value to calculate the muscles' PCSA). By creating a tridimensional representation of muscle fibers, we could add information that may help us to surpass the limitation of representing muscles as single line segments that remain unchanged in terms of shape when interacting with the underlying bones and other structures [22]. These observations strengthen the fact that muscle models should consider this parameter and use more than one single line segment to characterize the muscle structure. However, we observed that the different angles measured in the tracked fibers did not significantly alter the value of the PCSA. In fact, from all the architectural parameters, the pennation angle probably has a small influence on muscle PCSA calculations and, thus, in predicting the maximum muscle force [9]. After exercise, the pennation angle significantly decreased in the SOL muscle. This change may have been attributed to the challenging determination of the pennation angles in this particular muscle due to its multipennate architecture. It is quite difficult to determine all the preferential directions of the tracts and, of course, the mean value does not exactly show the individual fiber pennations.