Thermal Characterization of Ex Vivo Tissue.

Area of Science:

• Biomedical Engineering
• Computational Mathematics
• Oncology

Background:

• Mathematical modeling is crucial for cancer treatment strategies, requiring robust uncertainty quantification.
• Variability in tissue properties necessitates accounting for parameter uncertainties in thermal therapies like hyperthermia and thermal ablation.
• Accurate planning and control of these cancer treatments depend on understanding thermophysical tissue properties.

Purpose of the Study:

• To estimate thermophysical properties of ex vivo tissue.
• To apply inverse problem-solving techniques for parameter estimation.
• To implement uncertainty quantification for computational models in cancer therapy.

Main Methods:

• Utilized Markov chain Monte Carlo (MCMC) method.
• Employed approximate Bayesian computation (ABC) algorithm.
• Focused on solving inverse problems for ex vivo bovine muscle tissue.

Main Results:

• Successfully estimated thermophysical properties of ex vivo bovine muscle.
• Demonstrated the effectiveness of MCMC and ABC in solving inverse problems.
• Provided a framework for uncertainty quantification in thermophysical property estimation.

Conclusions:

• The study successfully estimated thermophysical properties of ex vivo tissue.
• Markov chain Monte Carlo and approximate Bayesian computation offer a robust framework for inverse problems and uncertainty quantification.
• Accurate thermophysical property estimation is vital for advancing cancer treatment modalities like hyperthermia and thermal ablation.