Accurate identification of contracting muscles can help us to understand the muscle function in both physiological and pathological conditions. Conventional electromyography (EMG) have limited access to deep muscles, crosstalk, or instability in the recordings. Accordingly, a novel framework was developed to detect contracting muscle regions based on the deformation field of transverse ultrasound images. We first estimated the muscle movements in a stepwise calculation, to derive the deformation field. We then calculated the divergence of the deformation field to locate the expanding or shrinking regions during muscle contractions. Two preliminary experiments were performed to evaluate the feasibility of the developed algorithm. Using concurrent intramuscular EMG recordings, Experiment I verified that the divergence map can capture the activity of superficial and deep muscles, when muscles were activated voluntarily or through electrical stimulation. Experiment II verified that the divergence map can only capture contracting muscles but not muscle shortening during passive movements. The results demonstrated that the divergence can individually capture the activity of muscles at different depths, and was not sensitive to muscle shortening during passive movements. The proposed framework can automatically detect the regions of contracting muscle, and could potentially serve as a tool to assess the functions of a group of muscles concurrently.

准确识别收缩肌肉可以帮助我们了解生理和病理状态下的肌肉功能。传统的肌电图 (EMG) 对深层肌肉、串扰或录音中的不稳定性的访问有限。因此，开发了一种新的框架来基于横向超声图像的变形场来检测收缩的肌肉区域。我们首先在逐步计算中估计肌肉运动，以推导出变形场。然后我们计算了变形场的散度，以定位肌肉收缩期间的扩张或收缩区域。进行了两个初步实验来评估所开发算法的可行性。使用并发肌内肌电图记录，实验一证实，当肌肉自愿或通过电刺激被激活时，发散图可以捕捉浅层和深层肌肉的活动。实验二验证了散度图只能捕捉被动运动过程中的收缩肌肉而不能捕捉肌肉缩短。结果表明，散度可以单独捕捉不同深度肌肉的活动，并且对被动运动过程中的肌肉缩短不敏感。所提出的框架可以自动检测收缩肌肉的区域，并有可能作为同时评估一组肌肉功能的工具。实验二验证了散度图只能捕捉被动运动过程中的收缩肌肉而不能捕捉肌肉缩短。结果表明，散度可以单独捕捉不同深度肌肉的活动，并且对被动运动过程中的肌肉缩短不敏感。所提出的框架可以自动检测收缩肌肉的区域，并有可能作为同时评估一组肌肉功能的工具。实验二验证了散度图只能捕捉被动运动过程中的收缩肌肉而不能捕捉肌肉缩短。结果表明，散度可以单独捕捉不同深度肌肉的活动，并且对被动运动过程中的肌肉缩短不敏感。所提出的框架可以自动检测收缩肌肉的区域，并有可能作为同时评估一组肌肉功能的工具。