Assessing Abdominal Muscle Composition via CT

After being transferred into a spatial database, CT images were processed using Analytic Morphomics, a semi-automated image analysis method that has been previously described^{23 ,36} . A combination of automated and user-guided algorithms written in Matlab (The Mathworks Inc, Natick, MA) identified the vertebral bodies to serve as an anatomical coordinate reference system. Next, the outer abdominal fascia and inner muscle wall were identified at all available vertebral levels to create enclosed regions of interest, which were confirmed by multiple trained researchers (Fig. 3).Figure 3

Example of healthy 20 y/o male T10-L5 axial CT slices showing SMA (blue-shaded area) between outer abdominal fascia (yellow line) and inner muscle wall (red line).

Sample size at each vertebral level varied due to differences in anatomy included in each scan. Measurements at T8 and T9 were excluded due to statistically significant differences in mean weight compared to those at T10 through L5. For T10-L5, there there were no significant differences in mean age, weight, or height within the male and female cohorts.
SMA was measured at the axial slice nearest the inferior aspect of each vertebral body as the area of pixels within −29 to +150 Hounsfield Units (HU) as previously validated^{21 (link),23 ,27} . Skeletal muscle index (SMI)–a heuristic that normalizes muscle area for height–was computed as SMA divided by height-squared³⁷ . Skeletal muscle radiation attenuation (SMRA) was computed as the mean Hounsfield Unit (HU) value of all pixels included in SMA^{27 ,38 (link),39 (link)}.