Numerical evaluation of the duodenal mucosal resurfacing technique for the treatment of Type 2 diabetes.

BACKGROUNDS AND OBJECTIVES: This work presents a numerical analysis of the Duodenal Mucosal Resurfacing (DMR) technique, which is a relatively new treatment for Type 2 diabetes that has been already tested in human beings. In this innovative strategy, an endoscopic catheter is placed inside the duodenum thus serving as a guide to an ablation device. A circumferential ablation is then performed by using a balloon filled with a hot fluid with pre- and post-cooling stages that allows for a controlled thermal procedure. Clinical outcomes indicate that the damaged duodenal lining induces a better control of glycemic levels. Therefore, a numerical evaluation of the efficiency of this treatment is carried out by utilizing the bioheat transfer equation in the transient form. METHODS: The finite volume method is used in the discretization of the energy equation and the results are verified by exploring the same mathematical model in a commercial finite-element package. The Arrhenius criterion for the evaluation of the thermally affected tissue is employed in this study. RESULTS: A systematic analysis of the simulations is performed by investigating two scenarios: one in which the lifting of the mucosal duodenum layer is achieved and another one where the lifting strategy is not implemented. The role of the magnitude of the blood perfusion coefficient, tissue thermal conductivity, peak, pre- and post-cooling temperatures is thoroughly explored, especially in connection with the evaluation of the extent of the thermally affected region. CONCLUSIONS: According to the simulations discussed in the present contribution, this treatment is capable of accurately targeting the cells in the mucosal layer without significantly affecting the outermost stratum of the organ if the lifting process is applied. However, for the case without lifting, the muscularis propria layer may reach temperatures above 42 °C during a short time interval and thus the treatment should be considered with caution by the physician.

Thermal Characterization of Ex Vivo Tissue.

Cancer treatment strategies require mathematical modeling of different coupled phenomena as well as uncertainty quantification of resulting computational solutions. Due to variability in thermophysical tissue properties among individuals, and even for the same individual under different physiological conditions, uncertainties in such parameters must appropriately be taken into account for accurate planning and control of hyperthermia and thermal ablation. The objective of this work is to estimate thermophysical properties of ex vivo tissue, with bovine muscle used for experiments. The Markov chain Monte Carlo method and approximate Bayesian computation algorithm are used to find solutions of the inverse problems examined in this work. These techniques provide a framework for not only solving the inverse problem but also finding uncertainty quantification.