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Related Experiment Video

Updated: May 17, 2026

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies
08:34

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Published on: February 6, 2019

Collimator position optimization for proton minibeam radiation therapy.

Nimita Shinde1, Yuting Lin1, Hao Gao2

  • 1Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.

Scientific Reports
|May 15, 2026
PubMed
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Proton minibeam (pMBRT) radiation therapy: experimental validation of Monte Carlo dose calculation in the RayStation TPS.

Physics in medicine and biology·2025

A new algorithm optimizes proton minibeam radiation therapy (pMBRT) by adjusting the multi-slit collimator (MSC) position. This improves dose distribution and organs-at-risk (OAR) sparing, enhancing treatment planning efficiency.

Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Computational Biology

Background:

  • Proton minibeam radiation therapy (pMBRT) uses spatially fractionated doses to minimize normal tissue toxicity.
  • The multi-slit collimator (MSC) is crucial for shaping proton beams into narrow minibeams.
  • Precise MSC positioning is vital for optimizing dose patterns and organ-at-risk (OAR) sparing.

Purpose of the Study:

  • To develop a novel collimator position optimization (CPO) algorithm for pMBRT.
  • To enable independent lateral shifts of the MSC at each beam angle for improved plan quality.
  • To integrate MSC position optimization into clinical treatment planning.

Main Methods:

  • Formulated the CPO problem as a mixed-integer programming (MIP) model, optimizing MSC positions and spot intensities.
Keywords:
Mixed integer programming (MIP)Multi-slit collimator (MSC)Proton minibeam radiotherapy (pMBRT)

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  • Employed an augmented Lagrangian framework with iterative convex relaxation and ADMM for solving the non-convex MIP problem.
  • Validated the algorithm on three clinical cases, comparing computation time and dosimetric outcomes against exhaustive enumeration.

    • Main Results:

    • The CPO algorithm achieved near-optimal solutions significantly faster than exhaustive methods (e.g., 700s vs. 15,000s).
    • Allowing multiple MSC positions per beam angle consistently improved dosimetry, especially OAR sparing.
    • Demonstrated a reduction in mean oral cavity dose from 6.5 Gy to 4.6 Gy in a head-and-neck cancer case.

    Conclusions:

    • The developed CPO algorithm efficiently enhances pMBRT plan quality.
    • Optimizing MSC positions leads to significant dosimetric improvements, particularly in OAR sparing.
    • The algorithm is suitable for clinical integration, improving treatment planning for pMBRT.