Quantifying wound morphology and tissue energetics for diagnosis and treatment of chronic wounds

The care and efficacy of treatment for chronic wounds is typically determined by observing and measuring the wound's response to a given treatment protocol. The traditional measures of wound morphology typically include photographs taken over time, alginates for determining wound volume, and rulers or concentric circles to estimate a wound's diameter. Although the traditional wound morphology measures are generally non-invasive, they are subjective and non-repeatable. Information on tissue response is generally limited to gross metabolic measurements acquired through standard diagnostic testing, bacteriological information from biopsied material and transcutaneous oximetry taken at the periphery of the wound. Information related to tissue response is generally acquired using invasive techniques. This paper describes a non-invasive method for assessing wound morphology and response being used to assess and study chronic wounds at the USAF Medical Center at Wright-Patterson AFB. This new technique exploits the properties of laser surface scanning and magnetic resonance spectroscopy to acquire its measurements. The method used employs a CyberwareTM laser surface scanner to capture both range and color information from the patient's wound surface. The color and range data are then registered to 1 mm accuracy for visualization of the patient's surface. The Magnetic Resonance Spectroscopy (MRS) data are then captured for the same wound using a surface localization and spectra collection protocol. The MRS data includes phosphorous MRS as an indicator of cellular energy balance. Spatial registration is used to combine the Cyberware and MRS datasets. The resulting data are then presented as a 3D volume with additional parameters, such as surface area, volume, and perimeter, portrayed for the total wound and specific tissue types. Results to date for our approach include the development of an automatic feature extraction algorithm that recognizes and extracts a wound edge from the laser surface scanner data. Additional tissue type characteristics, such as granulation, epithelialization, etc., are also identified by the feature extraction algorithm. The overall goal of this research is to provide a non-invasive, reliable method for wound quantification. Numerous patients with chronic wounds (i.e., diabetic ulcers, radiation wounds, etc.) require methods such as this for determining efficacy of wound treatments. These treatments often range from growth factors therapy to hyperbaric treatment for wound care. The results of this research will provide a technique for measuring some of the underlying biochemical mechanisms of wound healing in humans.