An Intensity-Similarity Coupling Framework for Extracting Motor Unit Twitch Area From Ultrafast Ultrasound Imaging

Accurate and non-invasive mapping of motor unit (MU) territories is essential for linking motoneuron activity with muscle contraction. Ultrafast ultrasound (UUS) enables high-resolution mechanical imaging of MUs; however, existing methods show limited spatiotemporal consistency of MU territories and lack sufficient validation. This study aims to develop a UUS-based approach to extract MU twitch areas with high spatiotemporal precision.

We propose P2P-R2: an automated two-step framework that integrates global intensity and temporal similarity features of muscular twitches to extract and refine MU twitch areas from spike-triggered averaged (STA) UUS data. To generate the STA data and provide validation, dual-probe UUS images and intramuscular EMG signals were concurrently recorded. To benchmark the proposed framework, multiple feature extraction strategies, including intensity-based, similarity-based, and previously published methods, were implemented and compared using spatial and temporal evaluation metrics.

P2P-R2 significantly outperformed all single-feature and existing methods, achieving higher within-region twitch consistency ($R^{2}_{s}$ = 0.96 $\pm$ 0.01) and between-probe twitch agreement ($R$ = 0.88 $\pm$ 0.26) than Naive STA ($R^{2}_{s}$ = 0.84 $\pm$ 0.19, $R$= -0.03 $\pm$ 0.62). It also reduced centroid-to-electrode distance (10.36mm $\pm$ 6.35mm) and improved spatial agreement (RoA = 0.09 $\pm$ 0.10). Furthermore, P2P-R2 captured complex MU activity patterns, including twisting, splits, and asynchronous motion.

P2P-R2 enables precise and robust MU twitch area extraction across both spatial and temporal domains. Its fully automated, source-agnostic design supports transition to fully non-invasive applications in neuromuscular diagnostics, motor unit tracking, and human-machine interfaces.