1D Thermoembolization Model Using CT Imaging Data for Porcine Liver.

Objective: Innovative therapies such as thermoembolization are expected to play an important role in improving care for patients with diseases such as hepatocellular carcinoma. Thermoembolization is a minimally invasive strategy that combines thermal ablation and embolization in a single procedure. This approach exploits an exothermic chemical reaction that occurs when an acid chloride is delivered via an endovascular route. However, comprehension of the complexities of the biophysics of thermoembolization is challenging. Mathematical models can aid in understanding such complex processes and assisting clinicians in making informed decisions. In this study, we used a Hagen-Poiseuille 1D blood flow model to predict the mass transport and possible embolization locations in a porcine hepatic artery. Method: The 1D flow model was used on imaging data of in-vivo embolization imaging data of three pigs. The hydrolysis time constant of acid chloride chemical reaction was optimized for each pig, and LOOCV method was used to test the model's predictive ability. Conclusion: This basic model provided a balanced accuracy rate of 66.8% for identifying the possible locations of damage in the hepatic artery. Use of the model provides an initial understanding of the vascular transport phenomena that are predicted to occur as a result of thermoembolization.

Modeling a 3-D multiscale blood-flow and heat-transfer framework for realistic vascular systems.

Modeling of biological domains and simulation of biophysical processes occurring in them can help inform medical procedures. However, when considering complex domains such as large regions of the human body, the complexities of blood vessel branching and variation of blood vessel dimensions present a major modeling challenge. Here, we present a Voxelized Multi-Physics Simulation (VoM-PhyS) framework to simulate coupled heat transfer and fluid flow using a multi-scale voxel mesh on a biological domain obtained. In this framework, flow in larger blood vessels is modeled using the Hagen-Poiseuille equation for a one-dimensional flow coupled with a three-dimensional two-compartment porous media model for capillary circulation in tissue. The Dirac distribution function is used as Sphere of Influence (SoI) parameter to couple the one-dimensional and three-dimensional flow. This blood flow system is coupled with a heat transfer solver to provide a complete thermo-physiological simulation. The framework is demonstrated on a frog tongue and further analysis is conducted to study the effect of convective heat exchange between blood vessels and tissue, and the effect of SoI on simulation results.

A Structured Cleaving Mesh for Bioheat Transfer Application.

Goal: The thermoregulation mechanism is a complex system that executes vital processes in the human body. Various models have been proposed to simulate the thermoregulatory response of an adult human to environmental stimuli. However, these models generally rely on stylized phantoms that lack the anatomical details of voxel phantoms used in radiation dosimetry and shielding research. The goal of this work is to introduce voxel phantoms to thermoregulation research by modeling the physical energy exchange between tissue and its surroundings, discuss a specific challenge associated with voxel phantoms, propose a method to address this challenge, and demonstrate its application. Method: One of the major challenges in using voxel phantoms is the stair-step effect on the surface of the voxelized domain. This effect causes over-estimation of surface area, accurate knowledge of which is critical for modeling heat exchanging systems. A methodology to generate a voxel domain from medical imaging data and reduce error in the surface area caused by the stair-step effect is presented. The methodology, based on a structured mesh and finite-volume method, is demonstrated with tumors generated from magnetic resonance imaging (MRI) scans of mice. Results: The methodology discussed in the paper shows a decrease in surface area over-estimation from 50% to 15% for a sphere and 47% to 17% for tumor models generated directly from MRI scans. Conclusion: This work provides a direct method to generate a smoother domain from medical imaging data and reducing surface area error in a voxelized domain. The technique presented is independent of domain material, including tissue type, and can be extended to any homogeneous or inhomogeneous domain. The increase in surface area accuracy obtained by smoothing the voxel domain results in more accurate temperature estimates in heat transfer simulation.