Towards rapid intraoperative axial localization of spinal cord ischemia with epidural diffuse correlation monitoring.

Spinal cord ischemia leads to iatrogenic injury in multiple surgical fields, and the ability to immediately identify onset and anatomic origin of ischemia is critical to its management. Current clinical monitoring, however, does not directly measure spinal cord blood flow, resulting in poor sensitivity/specificity, delayed alerts, and delayed intervention. We have developed an epidural device employing diffuse correlation spectroscopy (DCS) to monitor spinal cord ischemia continuously at multiple positions. We investigate the ability of this device to localize spinal cord ischemia in a porcine model and validate DCS versus Laser Doppler Flowmetry (LDF). Specifically, we demonstrate continuous (>0.1Hz) spatially resolved (3 locations) monitoring of spinal cord blood flow in a purely ischemic model with an epidural DCS probe. Changes in blood flow measured by DCS and LDF were highly correlated (r = 0.83). Spinal cord blood flow measured by DCS caudal to aortic occlusion decreased 62%. This monitor demonstrated a sensitivity of 0.87 and specificity of 0.91 for detection of a 25% decrease in flow. This technology may enable early identification and critically important localization of spinal cord ischemia.

Diffuse Correlation Spectroscopy Analysis Implemented on a Field Programmable Gate Array.

Diffusive correlation spectroscopy (DCS) is an emerging optical technique that measures blood perfusion in deep tissue. In a DCS measurement, temporal changes in the interference pattern of light, which has passed through tissue, are quantified by an autocorrelation function. This autocorrelation function is further parameterized through a non-linear curve fit to a solution to the diffusion equation for coherence transport. The computational load for this non-linear curve fitting is a barrier for deployment of DCS for clinical use, where real-time results, as well as instrument size and simplicity, are important considerations. We have mitigated this computational bottleneck through development of a hardware analyzer for DCS. This analyzer implements the DCS curving fitting algorithm on digital logic circuit using Field Programmable Gate Array (FPGA) technology. The FPGA analyzer is more efficient than a typical software analysis solution. The analyzer module can be easily duplicated for processing multiple channels of DCS data in real-time. We have demonstrated the utility of this analyzer in pre-clinical large animal studies of spinal cord ischemia. In combination with previously described FPGA implementations of auto-correlators, this hardware analyzer can provide a complete device-on-a-chip solution for DCS signal processing. Such a component will enable new DCS applications demanding mobility and real-time processing.