Evidence map›Paper›PMID 41756437›Full record

ArticleResearch square2026

Collimator position optimization for proton minibeam radiation therapy.

Nimita Shinde, Yuting Lin, Hao Gao

Abstract readPreprint
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Nimita ShindeDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Yuting LinDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Hao GaoDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.

Funding

Novel Optimization Methods and Treatment Planning System for Clinically-Deliverable Truly-Hybrid Proton-Photon RadiotherapyR37CA250921 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI Hao Gao · 2021 to 2026
$2.2M
Simultaneous dose and dose rate optimization for clinical FLASH proton radiotherapyR01CA261964 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI GAO, HAO · 2022 to 2025
$2.0M
NCI NIH HHS R01 CA261964NCI NIH HHS R37 CA250921
6 · The paper itself

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.

Indexed as

mixed integer programming (MIP)multi-slit collimator (MSC)proton minibeam radiotherapy (pMBRT)

Identifiers

PMID41756437
PMCPMC12934902

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.