Quantification of Spinal Cord Microvascular Perfusion utilizing Ultrasound

Approximately 900,000 spinal cord injuries (SCI) occur each year. Understanding the severity and progression of the injury can help tailor the treatment plan to optimize a patient's prognosis. One way to determine the progression of a neurological injury is to monitor the blood flow. While software exists for quantifying renal tissue perfusion with Doppler ultrasound, we developed an algorithm optimized to quantify perfusion in spinal cord microvasculature with multiple ultrasound imaging modalities. The objective of this study was to demonstrate spinal cord microvascular quantification methods using non-contrast ultrasound images. Following a T4-T6 laminectomy, ultrasound videos were captured of in vivo porcine spinal cords using color Doppler (CDI), advanced dynamic flow (ADF), and superb microvascular imaging (SMI) modalities. A MATLAB algorithm was developed to import ultrasound videos, extract the velocity map, and quantify the microvasculature blood flow as a function of time by averaging the velocity map in a region of interest. Using the velocity-time curve (VTC), local stroke volume (LSV) and local vascular output (LVO) were calculated. Our algorithm detected slow-flow (< 0.3 cm/s) changes indicative of cardiac cycles in each ultrasound modality for sub-millimeter diameter vessels. Each cardiac cycle from the VTC was extracted to calculate LSV and LVO. The mean ± 1 standard deviation LVO for CDI, ADF, and SMI were 0.23±0.008 mL/min, 0.28±0.003 mL/min, and 0.18±0.004 mL/min, respectively. Calculating these local perfusion metrics and tracking local perfusion after SCI may supplement current treatment by reducing the dependence on global measures of blood flow (e.g., mean arterial pressure).