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Updated: May 29, 2025
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Obtaining Quality Extended Field-of-View Ultrasound Images of Skeletal Muscle to Measure Muscle Fascicle Length
Published on: December 14, 2020
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UltraTimTrack: a Kalman-filter-based algorithm to track muscle fascicles in ultrasound image sequences
Tim J van der Zee1,2,3,4, Paolo Tecchio3, Daniel Hahn3,5
1Biomedical Engineering Graduate Program, University of Calgary, Calgary, Canada.
Peerj. Computer Science
|February 3, 2025
Summary
A new Kalman filter algorithm, UltraTimTrack, accurately tracks skeletal muscle fascicles in ultrasound images, providing low-noise and drift-free data for better muscle function analysis.
Area of Science:
- Biomechanics
- Medical Imaging
- Musculoskeletal Ultrasound
Background:
- Brightness-mode (B-mode) ultrasound non-invasively images skeletal muscle.
- Automatic fascicle tracking in ultrasound is challenging due to noise and drift.
Purpose of the Study:
- Develop a novel algorithm for drift-free, low-noise fascicle tracking.
- Improve accuracy across various experimental and imaging conditions.
Main Methods:
- Combined UltraTrack and TimTrack using a Kalman filter into the UltraTimTrack algorithm.
- Applied to medial gastrocnemius ultrasound sequences during dynamic contractions in healthy individuals.
Main Results:
- UltraTimTrack demonstrated drift-free tracking (2.1 mm fascicle length deviation) and low noise (1.4 mm cycle-to-cycle variability).
- Outperformed existing algorithms in accuracy and processing speed.
- Less affected by experimental variations than parent algorithms.
Conclusions:
- Developed a Kalman-filter-based algorithm for improved fascicle tracking from B-mode ultrasound.
- Provides low-noise, drift-free estimates of muscle architectural changes.
- Enhances interpretations of muscle function.
