UltraTimTrack: a Kalman-filter-based algorithm to track muscle fascicles in ultrasound image sequences

作     者：van der Zee, Tim J. Tecchio, Paolo Hahn, Daniel Raiteri, Brent J. 

作者机构：Univ Calgary Biomed Engn Grad Program Calgary AB Canada Univ Calgary Fac Kinesiol Calgary AB Canada Ruhr Univ Bochum Dept Human Movement Sci Bochum Germany Katholieke Univ Leuven Dept Movement Sci Leuven Belgium Univ Queensland Sch Human Movement & Nutr Sci Brisbane Australia 

出 版 物：《PEERJ COMPUTER SCIENCE》 (PeerJ Comput. Sci.)

年 卷 期：2025年第11卷

页      面：e2636-e2636页

核心收录：

基　　金：Deutscher Akademischer Austauschdienst Open Access Publication Funds of the Ruhr University Bochum 

主　　题：Medial gastrocnemius Muscle architecture Fascicle length Ultrasound imaging Ankle dynamometry B-mode ultrasonography Optical flow Hough transform Pennation angle Muscle contraction 

摘      要：Background Brightness-mode (B-mode) ultrasound is a valuable tool to non-invasively image skeletal muscle architectural changes during movement, but automatically tracking muscle fascicles remains a major challenge. Existing fascicle tracking algorithms either require time-consuming drift corrections or yield noisy estimates that require post-processing. We therefore aimed to develop an algorithm that tracks fascicles without drift and with low noise across a range of experimental conditions and image acquisition settings. Methods We applied a Kalman filter to combine fascicle length and fascicle angle estimates from existing and openly-available UltraTrack and TimTrack algorithms into a hybrid algorithm called UltraTimTrack. We applied the hybrid algorithm to ultrasound image sequences collected from the human medial gastrocnemius of healthy individuals (N = 8, four women), who performed cyclical submaximal plantar flexion contractions or remained at rest during passive ankle joint rotations at given frequencies and amplitudes whilst seated in a dynamometer chair. We quantified the algorithm s tracking accuracy, noise, and drift as the respective mean, cycle-to-cycle variability, and accumulated between-contraction variability in fascicle length and fascicle angle. We expected UltraTimTrack s estimates to be less noisy than TimTrack s estimates and to drift less than UltraTrack s estimates across a range of conditions and image acquisition settings. Results The proposed algorithm yielded low-noise estimates like UltraTrack and was drift-free like TimTrack across the broad range of conditions we tested. Over 120 cyclical contractions, fascicle length and fascicle angle deviations of UltraTimTrack accumulated to 2.1 +/- 1.3 mm (mean +/- sd) and 0.8 +/- 0.7 deg, respectively. This was considerably less than UltraTrack (67.0 +/- 59.3 mm, 9.3 +/- 8.6 deg) and similar to TimTrack (1.9 +/- 2.2 mm, 0.9 +/- 1.0 deg). Average cycle-to-cycle variability of UltraTimTrack was 1