Development and Application of a Custom Algorithm to Assess Hamstring Muscle Stiffness
Hamstring strain injuries (HSI) are the most diagnosed musculoskeletal injury among elite international track [and] field athletes. HSIs appear to occur most often during terminal swing phase of sprinting, placing track [and] field sprinters and jumpers at high risk of musculoskeletal injury. Repetitive uninterrupted bouts of eccentric loading movements, such as sprinting, can develop sites of microscopic damage within muscle fibers, acting as the origin of future muscular injury. It is unknown if the microscopic damage, occurring from repetitive sprinting movements, can be quantifiable via imaging technologies. Ultrasound shearwave elastography (SWE) is new imaging tool used to evaluate muscular tissue in vivo. However, the financial burden of this equipment can leave research labs with only a single machine, potentially limiting data collections and the ability to efficiently process data. Development of a custom SWE image processing algorithm will allow for data processing to be completed away from the ultrasound machine, resulting in the ability to perform more data collections. The purpose of this thesis is to develop and validate a custom image processing code and then implement that code to process a pre-existing dataset. Our custom image processing algorithm was validated successfully against the ultrasound machine in muscle, tendon, and ligament tissues, yielding excellent inter-day intraclass correlation coefficient (ICC) reliability values and excellent standard error measurement (SEM) values. We reported ICC (SEM) values of 0.999 (0.01 kPa), 0.999 (0.22 kPa), and 0.998 (0.46 kPa) for muscle, tendon, and ligament, respectively. Validated custom image processing algorithm was applied to a pre-existing dataset tracking hamstring muscle stiffness of track [and] field athletes. An outlier analysis revealed one participant suffered a grade I HSI, 55.69 kPa, 5.45 standard deviations above the mean. Ten more outlier data points were reported ranging 2.1-4.86 standard deviations above the mean values; however, these ten data points did not result in a diagnosed muscular injury. This thesis successfully created a custom SWE image processing algorithm that can be utilized to assess stiffness in muscle, tendon, or ligament tissue Our preliminary results reveal we can quantify and track changes in muscle material properties and regular monitoring of athletes can help identify those individuals at risk of muscular strain via SWE stiffness assessment.
East Carolina University