Comparison between transperineal ultrasound and digital dectection of levator ani trauma : can we improve the odds?

Aims To investigate the predictive ability of four digital assessment parameters to detect levator ani (LA) muscle defects (avulsion injury) and compare these to transperineal tomographic ultrasound images. Methods This was an observational study imbedded in a larger quasi-experimental cohort study for women with urinary incontinence. Seventy-two women, ≥60 years who had attended or were going to attend physiotherapy for treatment of urinary incontinence, were included in the study. Inclusion criteria from the parent study were symptoms of stress, urge or both types of urinary incontinence. The predictive ability of the following digital parameters: direct palpation of a discontinuity of the LA muscle from insertion on the pubic ramus; palpation of the distance between the muscle insertion sites; palpation of LA strength; palpation of LA tone, were analyzed against findings from tomographic transperineal ultrasound images. Correlation between methods was measured using Cohen's kappa for each of the individual parameters. Results Seventeen women (24%) presented with a complete or partial avulsion of the puborectalis muscle as diagnosed with tomographic ultrasound imaging. Nine women (13%) had complete avulsions, one of which was bilateral. The predictive ability of the digital assessment parameters varied from poor (k = 0.187, 95% CI [0.02–0.36]) to moderate (k = 0.569, 95% CI [0.31–0.83]). The new parameter of ‘width between insertion sites’ performed best. Conclusions Adding the parameter of “width between insertion sites” appears to enhance our ability to detect avulsion of the levator ani (LA) muscle by digital examination however it does not distinguish between unilateral or bilateral avulsion.

Feature-based tracking of urethral motion in low-resolution trans-perineal ultrasound

This paper describes a novel algorithm for tracking the motion of the urethra from trans-perineal ultrasound. Our work is based on the structure-from-motion paradigm and therefore handles well structures with ill-defined and partially missing boundaries. The proposed approach is particularly well-suited for video sequences of low resolution and variable levels of blurriness introduced by anatomical motion of variable speed. Our tracking method identifies feature points on a frame by frame basis using the SURF detector/descriptor. Inter-frame correspondence is achieved using nearest-neighbor matching in the feature space. The motion is estimated using a non-linear bi-quadratic model, which adequately describes the deformable motion of the urethra. Experimental results are promising and show that our algorithm performs well when compared to manual tracking.