Transient Thermal Response of Skin Tissue

The increased availability of thermal imaging cameras has led to a growing interest in the application of infrared imaging techniques to the detection and identification of subsurface structures. These imaging techniques are based on the following principle: when a surface is heated or cooled, variations in the thermal properties of a structure located underneath the surface result in identifiable temperature contours on it. These contours are characteristic of the structure’s shape, depth, and its thermal properties. We study the use of the transient thermal response of skin layers to determine to which extent the surface temperature distribution reflects the properties of subsurface structures, such as lesions. A numerical model using the finite element method is described to obtain this response and key results are reported in the paper. A sensitivity study is conducted first to better understand the thermal response of the system and the role of various system and model parameters. We explore the extent to which we are able to draw conclusions regarding the size, depth and nature of subsurface structures and accuracy of these conclusions based on the surface temperature response alone. This work validates the idea of examining the transient thermal response and using thermal imaging as a solution for lesion identification. A sensitivity study of surface temperature distribution to variations of thermophysical properties, blood perfusion rate, and thicknesses of skin layers is performed. It is observed that variations in these parameters have little impact on the surface temperature distribution. The work reported in the paper is a portion of a comprehensive research effort involving experiments on a phantom model as well as measurements on patients. Future work will focus on comparing the results of our 2D numerical modeling efforts with the experimental results using a skin tissue-mimicking phantom. Knowledge gained from the modeling and experimental efforts will be utilized in characterizing lesions in patient studies. The focus of this paper is the computational sensitivity analysis.