(C-108) Static and Dynamic Muscle Contractility Analysis During Treadmill Walking Using Ultrasound Imaging

Friday, October 13, 2023

3:30 PM – 4:30 PM PDT

Location: Exhibit Hall - Row C - Poster # 108

Presenting Author(s)

Anthea E. Spirko (she/her/hers)

Biomedical Engineering Student
Cornell University
Amherst, Massachusetts, United States

Co-Author(s)

QZ

Qiang Zhang, Ph.D. in Biomedical Engineering

NIDILRR ARRT Fellow, Co-mentor with Dr. Michael Lewek
NC State University/UNC at Chapel Hill, United States
ML

Ming Liu

Research Assistant Professor
University of North Carolina at Chapel Hill and North Carolina State University, United States

Primary Investigator(s)

HH

He (Helen) Huang

Jackson Family Distinguished Professor
University of North Carolina at Chapel Hill and North Carolina State University, United States

Introduction:
Ultrasound imaging provides a powerful tool for qualifying in-vivo muscular dynamics in humans, with fidelity to do so during functional activities such as walking and running. One of the essential steps is to track specific features in recorded ultrasound images, which is challenging due to the potentially low image quality, unclear morphological shape, and so on. Traditionally, analyzing cine B-mode ultrasound images requires time-intensive human labeling, disabling real-time applications if they have massive images. Modern advanced semi-automatic and fully-automatic approaches have been investigated to extract features from B-mode ultrasound images. For example, UltraTrack is a commercially-available software used for the dynamic tracking of muscle fascicles that allows the user to select two points, marking the endpoints of a fascicle, within a region of interest (ROI) [1]. One limitation of this method is that it requires clear morphological features such as muscle fascicles in images with high resolution, thus missing useful information provided by the rest of the muscle belly seen on the images. Instead of focusing on the clear shape of the images, in this study, we used speckle tracking to extract the average displacement of a certain region of the ultrasound images from plantar flexor muscles during treadmill walking, known as dynamic feature tracking. We found the advantages of using this dynamic feature tracking approach in terms of analyzing muscle deformation during walking by comparing the dynamic tissue displacement feature with a static feature, known as echo intensity signal.

Materials and Methods:

The participants of the study walked on an instrumented, split, two-belt treadmill (Bertec Corp., Columbus, OH, USA) with in-ground force plates going 1.0 m/s. An ultrasound transducer (38-mm of length, 6.4 MHz center frequency, L7.5SC Prodigy Probe, S-Sharp, Taiwan) was placed on the participant’s right calf, tracking both the lateral gastrocnemius (LGS) and soleus (SOL) muscles [4]. Ultrasound radio frequency data, collected at 1000 frames/s, was converted to B-mode images via beamforming and logarithmic compression. Each image measured 60 mm axially and 38 mm laterally, corresponding to 532 by 341 pixels. The frame count was reduced to 200 frames/s during data analysis.

Ultrasound imaging data analysis included static and dynamic methods for tracking the deformation of the ROI from plantar flexor muscles. The static method produced a one-dimensional echo intensity plot by averaging the grayscale values within the ROI of each ultrasound B-mode image. Grayscale values range from zero (black) to 255 (white) with lower numbers representing greater muscle contractility. Speckle tracking is a dynamic method of tracking muscle contractility that allows the selection of a larger region and then uses a correlation coefficient to track the region of greatest correlation [2,3]. The kernel was set as 25 by 15 pixels and the search window was set as 13 by 3 pixels. The speckle tracking algorithm first produced frame-to-frame displacement and then calculated the accumulated displacement of each pixel location within the ROI in both the axial and lateral directions.

Results, Conclusions, and Discussions:

Taking one gait cycle from a representative participant as an example, some basic results are shown in Fig. 1 to Fig. 3. The echo intensity signal (Fig. 1) follows a similar trend as the speckle tracking displacement signals (Fig. 2). The speckle tracking results provide more physically meaningful data for the actual average displacement over all pixels within the ROI of the LGS and SOL muscles in both the lateral and axial directions.

The point of peak contraction occurs at frame 147 on the echo intensity signal graph, marking 58.6% of the gait cycle. The peak averaged tissue displacement calculated during speckle tracking occurs at frame 149 with -2.08 mm in the axial direction and frame 153 with -1.56 mm in the lateral direction, which corresponds, respectively, to 59.4% and 60.9% of the gait cycle. Those peak contractility points align with the end of the stance phase of the right leg which occurs at 60% of the gait cycle, as shown in Fig. 3. The peak deformation of the plantar flexor muscles also corresponds to the end of the positive anterior-posterior GRF force that provides the peak forward propulsion impulse. After this time point, the toe-off occurs and the right leg enters the swing phase, where the muscle deformation recovers to the rest status.

The results indicate that the peak plantar flexor muscle deformation does not align with the peak net ankle joint torque, and the muscle deformation signals are relatively lagged to the net joint torque, similar to those in [5]. This finding provides evidence that muscle dynamics are different from corresponding joint dynamics, encouraging more studies on the inconsistency of muscle physiological changes and joint biomechanical functions during human walking. In addition, dynamic feature tracking from ultrasound images by using speckle tracking provides a powerful and precise tool to analyze muscle deformation in two dimensions rather than one. This can be used in conjunction with rehabilitation research to compare the biomechanics of able-bodied subjects to those with physical impairments to assist in the development of prosthetic and exoskeleton devices.

Acknowledgements (Optional): This work is supported by the Research Experience for Undergraduate (REU) Award provided by National Science Foundation through Neuromuscular Rehabilitation Engineering Laboratory (NREL) at the Joint Department of Biomedical Engineering UNC-NCSU.

References (Optional): [1] Farris et al. (2016), Comput. Methods Programs Biomed. 128; [2] Sheng et al. (2020), IEEE Trans. Biomed. Eng. 67(3); [3] Lubinski et al. (1999) IEEE Trans. Ultrason. Ferroelectr. Freq. Control 46(1); [4] Zhang et al. (2022), J NeuroEng. Rehabil. 19(86); [5] Nuckols et al. (2021), Sci. Robot. 6(60).