发表机构
University of Basel（巴塞尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出条件混合ResNet18-Transformer模型预测笔形束权重，以加速4D质子治疗计划生成，提高无龙门架扫描仪对移动靶区的治疗可行性和成本效益。
AI 中文摘要
目的：本研究旨在加速我们四维（4D）质子笔形束递送策略，该策略将呼吸运动纳入动态治疗计划，以提高无龙门架和无磁铁扫描仪设计的剂量适形性和治疗效率。方法：为加速4D治疗计划的生成和自适应，我们提出了一种条件混合ResNet18-Transformer模型来预测笔形束权重。该模型预测全部或部分笔形束权重。主要结果：条件模型能够成功预测治疗的剩余束权重，结果表明将该方法扩展到全治疗预测是可行的，但需要进一步研究。意义：通过笔形束权重预测加速4D治疗生成，使我们更接近用简化的无龙门架和无磁铁扫描仪治疗移动目标的可行性。在保持剂量适形性的同时降低系统复杂性，可能为运动影响肿瘤提供更可及且成本效益更高的质子束治疗途径。我们的代码可在此处获取：此HTTPS URL。
英文摘要
Objective. This work aims to accelerate our four-dimensional (4D) proton pencil beam delivery strategy, which incorporates respiratory motion into a dynamic treatment plan, to improve dose conformity and treatment efficiency for gantry-less and magnet-free scanner designs. Approach. To accelerate the generation and adaptation of 4D treatment plans, we propose a conditional hybrid ResNet18-Transformer model for predicting pencil beam weights. The model predicts either the full set of pencil beam weights or a subset thereof. Main Results. The conditional model can successfully predict the remaining beam weights of a treatment, and the results suggest that extending this approach to full treatment prediction is feasible but requires further investigation. Significance. The acceleration of 4D treatment generation via pencil beam weight prediction takes us one step closer to the feasibility of treating mobile targets with simplified, gantry-less, and magnet-free scanner designs. Reducing system complexity while preserving dosimetric conformity may offer a pathway toward more accessible and cost-effective proton beam therapy for motion-affected tumours. Our code is available here: https://github.com/NairVonMuehlenen/Pencil-Beam-Weight-Prediction
Comments10 pages, 4 figures
