Development and Validation of a Noninvasive Multimodal Ultrasound-Based Imaging Biomarker for Myofascial Pain

The investigators propose to use multimodal ultrasound imaging to develop and validate a practical and inexpensive biomarker for lumbar myofascial pain, which shows sensitivity to change in relation to treatment. Myofascial pain (MP) is a frequent contributing factor to chronic low back pain (cLBP). It is associated with a range of tissue abnormalities, such as taught muscle bands, trigger points (TPs), and thoracolumbar fascia motion dysfunction, along with poor tissue elasticity. As a result, a composite biomarker for MP related to components of the syndrome is more likely to be plausible biologically, robust, and useful clinically for diagnosis and treatment. The investigators propose to study: 1. The echogenicity of latent and active trigger points, 2. The dynamic spatial-temporal tissue deformation quantified by strain tensors (compression, extension, and shear) in the thoracolumbar fascia and multifidus muscle, 3. The viscoelastic properties of the fascia and muscles measured by ultrasound shear wave elastography. In the R61 Phase (year 1 to 3) the investigators will use deep learning to integrate these measurements into a predictive biomarker and use established validation methods to test its ability to predict MP.

The investigators will determine the sensitivity and specificity of the biomarker to classify the myofascial components of pain, as well as the response to treatment (a diagnostic and predictive marker).