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Monte Carlo methods for medical imaging research.

Hoyeon Lee1

  • 1Department of Diagnostic Radiology and Centre of Cancer Medicine, University of Hong Kong, Hong Kong, China.

Biomedical Engineering Letters
|October 28, 2024
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Summary

Computational modeling with Monte Carlo methods accurately simulates radiation-based medical imaging systems and algorithms. This review covers Monte Carlo physics, applications, codes, and future research directions in medical imaging.

Keywords:
Computational modelingMedical imagingMonte Carlo

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Area of Science:

  • Medical Physics
  • Computational Science
  • Radiological Imaging

Background:

  • Computational modeling is crucial for designing and validating radiation-based medical imaging systems.
  • Monte Carlo (MC) methods are favored for their accurate and intuitive simulation of particle interactions.

Purpose of the Study:

  • To review the physics principles underlying Monte Carlo methods.
  • To explore the diverse applications of Monte Carlo in medical imaging research.
  • To discuss available Monte Carlo codes and future research avenues.

Main Methods:

  • Review of fundamental physics principles of Monte Carlo simulations.
  • Survey of existing literature on Monte Carlo applications in medical imaging.
  • Compilation of currently available Monte Carlo software codes.

Main Results:

  • Monte Carlo methods offer high fidelity in modeling particle transport and interactions.
  • Numerous applications exist in areas like dose calculation, image reconstruction, and system optimization.
  • A range of Monte Carlo codes are available, each with specific strengths.

Conclusions:

  • Monte Carlo methods are indispensable tools for advancing radiation-based medical imaging.
  • Further research can optimize MC simulations for enhanced imaging performance and novel applications.
  • The review provides a resource for researchers utilizing or exploring Monte Carlo techniques.