Background: Musculoskeletal ultrasound (MUS) is an inexpensive method to assess age-related changes in muscle tissue composition. Statistical modeling of MUS image data is relatively unexplored and may reveal correlates of muscle function and quality. The primary objective of this study was to determine how well several statistical models fit MUS image data. The secondary objective was to assess the association between model parameters and muscle performance in young and older adults. Methods: Seventeen young (age = 24 yrs. ± 2) and seventeen older (age = 65 yrs. ± 7) adults enrolled in the study. Ultrasound scans of the rectus femoris muscle were obtained using B-mode MUS with a 13-6 MHz linear array transducer. For each scan, grayscale data were extracted from a region encompassing the muscle, and parameters for the normal, Poisson, and negative binomial distributions were estimated. Theoretical data were generated from parameter estimates, and R2 values were computed to assess agreement between grayscale and theoretical data. A one-way ANOVA was used to test for differences between each statistical model and F-tests were performed to compare goodness-of-fit. Muscle performance was measured with a hand dynamometer, and correlation analysis was conducted to determine the association between hand grip strength and parameter estimates. Results: Mean R2 values were similar between the negative binomial (R2 = 0.93 ± 0.06) and normal (R2 = 0.84 ± 0.10) distributions (p = 0.141) and both demonstrated good agreement with grayscale data. The Poisson distribution had poor agreement (R2 = -0.34 ± 0.60) was dissimilar to the other models (p