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Tumor parameter estimation considering the body geometry by thermography.

Shazzat Hossain1, Farah A Mohammadi1

  • 1Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada M5B 2K3.

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|July 15, 2016
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Summary

This study uses finite element analysis (FEA) and a custom genetic algorithm (GA) to correlate surface temperature with internal physiology for non-invasive thermal diagnostics. The method accurately predicts tumor characteristics like depth, size, and metabolic rate.

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Computational Biology

Background:

  • Non-invasive, non-contact thermal diagnostics require accurate links between surface temperature and internal physiological states.
  • Understanding bio-heat transfer in various organs, including the breast, is crucial for developing these diagnostic tools.
  • Existing methods may lack the precision to quantify underlying physiological parameters from thermal data.

Purpose of the Study:

  • To develop and validate an evolutionary methodology for forecasting physiological-thermo-biological parameters using non-invasive thermal data.
  • To establish quantitative correlations between surface temperature and internal organ physiology, specifically for detecting nodules.
  • To create anatomically accurate finite element analysis (FEA) models for simulating bio-heat phenomena in different body parts.

Main Methods:

  • Utilized finite element analysis (FEA) to create anatomically accurate models of organs (chest, forearm, breasts).
  • Developed a custom Genetic Algorithm (GA) to parameterize and identify unknown physiological parameters by minimizing a fitness function.
  • Generated simulated datasets and thermograms, incorporating ±10% random noise to mimic real-world data variability.

Main Results:

  • Successfully established quantitative links between surface temperature and internal physiological parameters.
  • The evolutionary methodology, combined with FEA and GA, accurately predicted the depth, size, and metabolic rate of simulated nodules.
  • Simulated thermograms with noise demonstrated the robustness of the developed method in parameter estimation.

Conclusions:

  • The proposed evolutionary methodology offers a promising approach for non-invasive thermal diagnostics by accurately correlating surface temperature with internal physiology.
  • FEA modeling and GA-based parameterization are effective tools for forecasting critical physio-thermo-biological parameters of internal anomalies.
  • This research advances the development of radiation-free diagnostic tools for early detection and characterization of subsurface pathologies.