Poster R6 - The Application of Quantitative Ultrasound Analysis in Assessing Pelvic Floor Muscles

Introduction: The levator ani muscle is a functionally important pelvic support structure. Injury of the levator ani muscle is a significant factor in developing pelvic floor disorders that affect 37% of all women. Advances in diagnostic imaging have enabled noninvasive evaluation of the health and functional state of the muscles that support the pelvic floor, such as the levator ani muscle. In this study, we examine the feasibility of using quantitative ultrasound analysis (QUS) to assess women’s pelvic floor muscle injuries. Quantitative ultrasound analysis utilizes statistical models to characterize ultrasound echo-textures and provides a comprehensive understanding of tissue microstructure. Quantification of pelvic floor muscle microstructures using QUS has the potential to provide objective and quantitative evaluation of muscle injury.

Materials and

Methods: In our study, we employed quantitative ultrasound analysis, specifically ultrasound texture envelope analysis, on volumetric B-mode ultrasound data of the levator ani muscle from a sample of nine subjects. We fitted three statistical models, the Nakagami distribution, the gamma distribution, and the mixture of two gamma distributions, to the muscle intensity histograms. The nine subjects were classified into two groups based on their Levator Ani Muscle Deficiency (LAD) scores assessed visually: those with no or little defect (LAD ≤ 1) and those with moderate or severe defect (LAD > 1). We then examined the differences in the estimated model parameter values between the two groups using the Wilcoxon rank sum test with a significance level set at 0.05.

Results, Conclusions, and Discussions: The chi-square goodness-of-fit tests showed that the mixture of two gamma distributions was able to most closely approximate the pixel intensity histogram. The shape parameters of all three fitted models significantly differed between the two groups (p < 0.01). In contrast, the mean and standard deviation of the pixel intensities did not differ by group, suggesting the necessity of the proposed work on using more advanced statistical models to better characterize the levator ani muscle microstructure using ultrasound images.

Our findings, which provide preliminary evidence that QUS parameters can be used in the image-based classification of normal and injured levator ani muscles, underscore the potential of this approach in the clinical assessment of pelvic floor injuries. However, further analysis of a larger population is needed to validate and generalize these observations.

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