A Structured Cleaving Mesh for Bioheat Transfer Application

Abstract

The human body executes a complex control scheme in an attempt to maintain a constant core temperature. The hypothalamus acts as a thermostat that receives input signals from different parts of the body and responds with different control mechanisms to regulate heat exchange and create a homeothermic core. This entire process of receiving input signals and providing feedback response is called the human thermoregulation mechanism. Thermophysiological models such as the Fiala model [1], [2] are used to simulate this process to understand the response of the human body to its thermal surroundings. The Fiala and other models used in thermophysiological and human thermal comfort studies rely on stylized phantoms [3]. A parallel branch of research that deals with biomedical applications [4] and radiation dosimetry [5]–[6][7] makes use of computational human phantoms (CHPs). CHPs initially consisted of stylized phantoms, which were made using simple geometrical objects. With advancements in technology and available resources, the stylized phantoms were largely replaced by voxel phantoms and later by a family of hybrid phantoms. Voxel phantoms are generated from medical imaging data and provide a detailed and accurate representation of human anatomy. Hybrid phantoms are constructed using non-uniform rational B-spline (NURBS) surfaces and polygon mesh elements that provide a smooth surface for organs and the entire human body. Voxel phantoms present the challenge of a stair-step effect on curved surfaces, especially for smaller organs like eye balls, due to the voxel shape [4]. Tetrahedral mesh-based (TM-based) CHPs are used as a solution to overcome the challenges of hybrid and voxel phantoms [4], [8]. The present study focuses on using a structured form of the cleaving mesh to (1) generate a simulation domain directly from medical imaging data of the patient and (2) perform heat-transfer simulations using this domain. This method makes generation of a mesh from medical imaging data straightforward, without any need for conversion to other phantom surface representations [4], [8]. The remainder of the paper is organized into sections that outline the proposed methodology and its significance. Section II provides a short review of models used in human thermophysiological research. Challenges associated with use of these models point to the need for a model that is based on accurate representation of human anatomy.