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Optimisation in radiotherapy. II: Programmed and inversion optimisation algorithms

M Ebert1

  • 1Department of Medical Physics, Royal Perth Hospital, Western Australia. martin.ebert@nero.rph.health.wa.gov.au

Australasian Physical & Engineering Sciences in Medicine
|March 21, 1998
PubMed
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This study explores radiotherapy optimization algorithms. It details mathematical programming and numerical inversion techniques for finding optimal irradiation strategies, building on previous foundational concepts.

Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Computational Biology

Background:

  • This article follows foundational work on radiotherapy optimization and problem formulation.
  • It is the second in a three-part series on radiotherapy optimization.

Purpose of the Study:

  • To outline algorithms used for optimizing radiotherapy treatment planning.
  • To explore methods for identifying the best irradiation strategies.

Main Methods:

  • Review of mathematical programming algorithms, including linear programming and artificial intelligence search techniques.
  • Discussion of numerical inversion methods, specifically deterministic iterative inversion.
  • Examination of algorithms for searching the space of possible radiotherapy strategies.

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Main Results:

  • Identified two primary classes of algorithms for radiotherapy optimization: mathematical programming and numerical inversion.
  • Detailed specific search techniques within these algorithmic classes.
  • Provided an overview of methods for solving the radiotherapy optimization problem.

Conclusions:

  • Algorithm selection is crucial for effective radiotherapy optimization.
  • Both mathematical programming and numerical inversion offer distinct approaches to finding optimal treatment plans.
  • Further exploration of these algorithms can advance radiotherapy precision.