ArticleResearch square2026
Collimator position optimization for proton minibeam radiation therapy.
Article in Research square, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Proton minibeam radiation therapy (pMBRT) employs spatially fractionated dose distributions to reduce normal tissue toxicity. A key component is the multi-slit collimator (MSC), which shapes the beam into narrow, spatially separated minibeams. Small lateral shifts of the MSC relative to the beam direction can substantially alter peak-valley dose patterns, target coverage, and organs-at-risk (OAR) sparing, making MSC positioning a critical planning parameter. We develop a novel collimator position optimization (CPO) algorithm for pMBRT that allows independent lateral shifts of the MSC at each beam angle to improve plan quality. The problem is formulated as a mixed-integer programming (MIP) model that jointly optimizes MSC positions and spot intensities. Binary variables select candidate lateral shifts per beam angle, while continuous variables represent spot intensities. The resulting non-convex problem is solved using an augmented Lagrangian framework with iterative convex relaxation and alternating direction method of multipliers (ADMM) decomposition. In three clinical cases, the proposed method achieved near-optimal solutions with substantially reduced computation time compared to exhaustive enumeration (e.g., 700 s vs. 15,000 s for an abdominal case). Allowing multiple MSC positions per beam angle led to consistent dosimetric improvements, particularly in OAR sparing; for example, mean oral cavity dose in a head-and-neck case decreased from 6.5 Gy to 4.6 Gy. MSC position optimization enhances pMBRT plan quality and can be efficiently integrated into clinical treatment planning.
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