UCLA researchers in the Department of Bioengineering have developed an algorithm to assess the burn wound severity and predict its future outcomes using Terahertz imaging.
Quantitatively assessing burn wound severity and predicting how the wound heals is important for timely and accelerated therapeutic interventions. However, currently available imaging techniques have limited capabilities to perform such an analysis. Optical coherence tomography is only limited to assessing the morphological features of the wound, while assessing wound severity through Doppler processing of coherent laser illumination is confounded by the presence of edema in the wound tissue. Terahertz imaging has been shown to be a promising technique for assessing the burn wound status due to its high sensitivity and specificity to tissue water content. However, currently there is no technique that can spatially delineate different regions within the burn tissue and determine the future outcomes of the wound.
Dr. Zachary Taylor and colleagues have developed an algorithm that can process terahertz images to assess skin-burn wound status and spatially delineate different regions within the wound as the regions that will heal, regions that require intervention, and regions that are permanently damaged. The algorithm exploits the high contrast sensitivity of terahertz reflectivity maps to differential edema distribution in an acute wound to spatially delineate the wound status. The algorithm’s spatial diagnosis can be co-registered with a visible image to facilitate clinical decision-making. Unlike other imaging techniques, the algorithm is robust to the presence of edema and tissue swelling in acute burn injuries.
The proposed algorithm can be potentially applied to spatially delineate different regions in the skin-burn wounds based on the severity of the wound and wound healing status. It can be potentially extended to assessing skin wounds in other pathologies including:
A working algorithm exists. The algorithm has been successfully tested in rats to differentiate partial thickness burns from full thickness burns. Results from the algorithm have been validated using histology.
|United States Of America||Published Application||20190082998||03/21/2019||2015-149|