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A quantum computing approach to beam angle optimization.

Nimita Shinde1, Ya-Nan Zhu1, Haozheng Shen2

  • 1Department of Radiation Oncology, University of Kansas Medical Center, USA.

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Summary
This summary is machine-generated.

This study introduces QC-BAO, a hybrid quantum-classical method that improves radiation therapy planning by optimizing beam angles. QC-BAO enhances treatment quality and reduces organ-at-risk doses, showcasing quantum computing

Keywords:
beam angle optimizationmixed integer programmingquantum optimization

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Area of Science:

  • Medical Physics
  • Quantum Computing
  • Radiation Therapy

Background:

  • Beam Angle Optimization (BAO) is crucial for radiation therapy (RT) treatment planning, particularly for proton RT.
  • BAO is a computationally challenging mixed integer programming (MIP) problem due to its NP-hard nature and large search space.
  • Existing optimization techniques face limitations in computational efficiency, driving the need for novel approaches.

Purpose of the Study:

  • To introduce QC-BAO, a novel hybrid quantum-classical approach for solving the MIP formulation of BAO.
  • To leverage quantum computing to address the complexity of beam angle selection in radiation therapy planning.

Main Methods:

  • QC-BAO models BAO as an MIP problem using binary variables for beam angle selection and continuous variables for spot intensity optimization.
  • A hybrid quantum-classical framework is employed, utilizing quantum computing for binary decisions and classical methods (iterative convex relaxation, ADMM) for continuous optimization.
  • The approach is evaluated on clinical test cases using proton therapy parameters.

Main Results:

  • QC-BAO demonstrated superior treatment plan quality compared to clinically verified angles and a heuristic method (GS-BAO).
  • The method consistently improved the conformity index (CI) for target coverage and reduced doses to organs-at-risk (OAR).
  • In a lung cancer case, QC-BAO achieved a higher CI (0.89 vs. 0.85/0.76) and lower mean lung dose (2.85 Gy vs. 3.36/4.80 Gy).

Conclusions:

  • The findings underscore the potential of quantum computing to significantly enhance BAO in radiation therapy.
  • QC-BAO offers improved treatment plan quality, paving the way for future clinical implementation of quantum-accelerated optimization in RT.