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Computational study on 2D three-phase lag bioheat model during cryosurgery using RBF meshfree method.

Rohit Verma1, Sushil Kumar1

  • 1Department of Mathematics, S.V. National Institute of Technology, Surat 395007 Gujarat, India.

Journal of Thermal Biology
|June 21, 2023
PubMed
Summary

This study models cryosurgery using a three-phase lag bioheat model to predict temperature distribution and freezing fronts in biological tissues. The findings offer insights into optimizing thermal damage for cancer treatment.

Keywords:
Bioheat equationEffective heat capacityIrregular domainPhase change interfaceRadial basis functionsThree phase lag

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Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Oncology

Background:

  • Biological tissues possess complex, non-homogeneous structures affecting thermal properties.
  • Accurate temperature distribution and freezing front prediction are vital for effective cryosurgery.
  • Existing mathematical models require enhancement for precise cryosurgery simulations.

Purpose of the Study:

  • To numerically investigate phase change phenomena in biological tissues during cryosurgery.
  • To analyze the influence of the three-phase lag (TPL) bioheat model on thermal behavior.
  • To simulate cryosurgery in diverse tissue geometries (circular, ameba-like, multiconnected).

Main Methods:

  • Utilized the effective heat capacity formulation to solve the nonlinear governing equation.
  • Employed Gaussian radial basis function for spatial discretization.
  • Applied Crank-Nicolson finite difference approximation for temporal discretization.

Main Results:

  • Investigated the impact of phase lag (τv) on thermal distribution and phase change interface.
  • Simulated cryosurgery effects across circular, ameba-like, and multiconnected soft tissue models.
  • Demonstrated the algorithm's capability to predict thermal dynamics in complex tissue structures.

Conclusions:

  • The three-phase lag bioheat model provides a robust framework for simulating cryosurgery.
  • Phase lag significantly influences thermal distribution and freezing front progression.
  • These findings can aid in optimizing cryosurgical techniques for oncological applications.