Computational methods for quantifying in vivo muscle fascicle curvature from ultrasound images

Muscle fascicles curve during contraction, and this has been seen using B-mode ultrasound. Curvature can vary along a fascicle, and amongst the fascicles within a muscle. The purpose of this study was to develop an automated method for quantifying curvature across the entirety of an imaged muscle, to test the accuracy of the method against synthetic images of known curvature and noise, and to test the sensitivity of the method to ultrasound probe placement. Both synthetic and ultrasound images were processed using multiscale vessel enhancement filtering to accentuate the muscle fascicles, wavelet-based methods were used to quantify fascicle orientations and curvature distribution grids were produced by quantifying local curvatures for each point within the image. Ultrasound images of ramped isometric contractions of the human medial gastrocnemius were acquired in a test-retest study. The methods enabled distinct curvatures to be determined in different regions of the muscle. The methods were sensitive to kernel sizes during image processing, noise within the image and the variability of probe placements during retesting. Across the physiological range of curvatures and noise, curvatures calculated from validation grids were quantified with a typical standard error of less than 0.026 m⁻¹, and this is about 1% of the maximum curvatures observed in fascicles of contracting muscle.