A mixed integer programming approach to minibeam aperture optimization for multi‐collimator proton minibeam radiotherapy

Background Multi‐collimator proton minibeam radiation therapy (MC‐pMBRT) has recently emerged as a versatile technique for dose shaping, enabling the formation of peak‐valley dose patterns in organs‐at‐risk (OAR) while maintaining a uniform dose distribution in tumor targets. MC‐pMBRT leverages a se...

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Veröffentlicht in:Medical physics (Lancaster) Jg. 52; H. 11; S. e70129 - n/a
Hauptverfasser: Shinde, Nimita, Zhang, Weijie, Lin, Yuting, Gao, Hao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.11.2025
Schlagworte:
Humans
IMPT
inverse optimization
mixed‐integer programming
Organs at Risk - radiation effects
peak‐to‐valley dose ratio (PVDR)
proton minibeam radiotherapy (pMBRT)
Proton Therapy - instrumentation
Proton Therapy - methods
Radiotherapy Dosage
Radiotherapy Planning, Computer-Assisted - methods
mixed‐integer programming
proton minibeam radiotherapy (pMBRT)
IMPT
peak‐to‐valley dose ratio (PVDR)
inverse optimization
ISSN:0094-2405, 2473-4209, 2473-4209
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Zusammenfassung:Background Multi‐collimator proton minibeam radiation therapy (MC‐pMBRT) has recently emerged as a versatile technique for dose shaping, enabling the formation of peak‐valley dose patterns in organs‐at‐risk (OAR) while maintaining a uniform dose distribution in tumor targets. MC‐pMBRT leverages a set of generic multi‐slit collimators (MSCs), each with different center‐to‐center (ctc) distances. However, the current method for minibeam aperture optimization (MAO), that is, the selection of MSC per beam angle to optimize plan quality, is manual and heuristic, resulting in computational inefficiencies and no guarantee of optimality. Purpose This work introduces a novel mixed integer programming (MIP) approach to MAO for optimizing MC‐pMBRT plan quality. Methods The proposed MIP approach jointly optimizes dose distributions and peak‐to‐valley dose ratio (PVDR) and selects the optimal set of MSC per beam angle. The optimization problem includes decision variables for MSC selection per beam angle and spot weights. The proposed MIP approach is a two‐step process: in the first step, the values of binary variables are determined to select MSC for each beam angle; in the second step, the continuous variables are solved to determine the spot weights. Both steps utilize iterative convex relaxation (ICR) and the alternating direction method of multipliers (ADMM) to solve the optimization problems efficiently. Results The proposed MIP method to solve MAO (MIP‐MAO) was validated against the conventional heuristic method (CONV) for MC‐pMBRT treatment planning. Results indicate that MIP‐MAO enhances the conformity index (CI) for the target and improves PVDR for OAR. For instance, in a head‐and‐neck (HN) case, CI improved from 0.61 (CONV) to 0.70 (MIP‐MAO); in an abdomen case, CI improved from 0.78 (CONV) to 0.83 (MIP‐MAO). Additionally, MIP‐MAO reduced mean doses in the body and OAR. Conclusions A novel MIP approach for MAO in MC‐pMBRT is presented, showing demonstrated improvements in plan quality and PVDR compared to the heuristic method.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0094-2405
2473-4209
2473-4209
DOI:10.1002/mp.70129