Estimating skeletal muscle fascicle curvature from B-mode ultrasound image sequences.

We address the problem of tracking in vivo muscle fascicle shape and length changes using ultrasound video sequences. Quantifying fascicle behavior is required to improve understanding of the functional significance of a muscle's geometric properties. Ultrasound imaging provides a noninvasive means of capturing information on fascicle behavior during dynamic movements; to date however, computational approaches to assess such images are limited. Our approach to the problem is novel because we permit fascicles to take up nonlinear shape configurations. We achieve this using a Bayesian tracking framework that is: 1) robust, conditioning shape estimates on the entire history of image observations; and 2) flexible, enforcing only a very weak Gaussian Process shape prior that requires fascicles to be locally smooth. The method allows us to track and quantify fascicle behavior in vivo during a range of movements, providing insight into dynamic changes in muscle geometric properties which may be linked to patterns of activation and intramuscular forces and pressures.