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Non-invasive characterization of tumors using Bayesian inference and Virtual Element Method.

Pugazhenthi Thananjayan1, M Arrutselvi2, Sundararajan Natarajan2

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Computer Methods and Programs in Biomedicine
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This study introduces a computational framework using Bayesian inference and the Virtual Element Method (VEM) for non-invasive tumor detection. The method accurately identifies single and multiple tumors using surface temperature data, offering improved reliability for clinical diagnostics.

Keywords:
Bayesian inferenceTumor detectionUncertainty quantificationVirtual Element Method

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Area of Science:

  • Biomedical Engineering
  • Computational Modeling
  • Medical Imaging

Background:

  • Non-invasive tumor detection is crucial but challenging.
  • Traditional methods have limitations in resolution and invasiveness.
  • Surface temperature measurements offer a potential non-invasive data source.

Purpose of the Study:

  • To develop and validate a computational framework for non-invasive tumor characterization.
  • To utilize Bayesian inference and the Virtual Element Method (VEM) for inverse problem solving.
  • To assess the framework's performance in detecting and quantifying single and multiple tumors.

Main Methods:

  • Modeled tissue thermal response using Pennes' bioheat equation.
  • Employed Metropolis-Hastings (M-H) and Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithms for inference.
  • Simulated scenarios included single tumors, tumor clusters, and multiple tumors with varying noise levels.

Main Results:

  • Successfully detected and quantified single tumors and tumor clusters using M-H algorithm.
  • Accurately estimated parameters for multiple tumors simultaneously using RJMCMC algorithm.
  • Demonstrated robustness to measurement noise for reliable tumor detection and characterization.

Conclusions:

  • Bayesian inference with VEM offers a flexible and powerful computational framework for tumor detection.
  • The approach enhances thermal-based tumor detection with improved reliability and adaptability.
  • Provides uncertainty quantification through credible intervals, valuable for clinical interpretation.