发表机构
Emory University; University of Chicago（埃默里大学; 芝加哥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于 foundation-model 配准的数字孪生框架，通过跨患者迁移解剖变化合成预测CT，可提升头颈自适应质子治疗的匹配度，无需重复成像。
AI 中文摘要
头颈（HN）质子治疗对4至6周疗程内的解剖结构变化高度敏感，肿瘤缩小、体重下降及摆位误差可能使布拉格峰错位至腮腺、口腔、脑干、脊髓等关键器官附近，导致靶区欠量或危及器官过量受量。在线自适应质子治疗会根据当日解剖结构重新制定计划，但标准流程依赖离线重新计划，需多次采集CT并耗时约一周准备，增加了负担、成本与延迟。本研究探究能否通过从人群数据库迁移纵向变化，在图像采集前预测患者治疗日的解剖结构。我们提出一种基于预训练 foundation-model 可变形配准网络的数字孪生框架，无需患者特定训练：第一次配准将患者的计划CT与目标对齐，并将该患者治疗期间的质量保证CT（QACT）转换至目标坐标系；第二次配准估计该患者计划CT至QACT的变化，再将此变化应用于目标患者自身的计划CT，合成带有传播轮廓的预测CT（pdCT）。使用88名HN患者（每人含1份计划CT和3份QACT）的研究显示，pdCT较静态计划CT更匹配治疗日解剖结构：与单独计划CT相比，归一化互相关提升22.8%，危及器官的Dice系数提升20.2%，CT值误差降低23.4%；在解剖结构变化显著的患者中增益最大，解剖结构稳定时增益可忽略。这种跨患者运动迁移利用数字孪生概念预测治疗日解剖结构，无需重复成像即可实现个性化在线自适应质子治疗。
英文摘要
Head-and-neck (HN) proton therapy is highly sensitive to anatomical change over a 4-to-6-week course, as tumor shrinkage, weight loss, and setup variation can misposition the Bragg peak near critical organs such as the parotids, oral cavity, brainstem, and spinal cord, leading to target underdosing or organ-at-risk overdosing. Online adaptive proton therapy replans on the anatomy of the day, yet standard workflows rely on offline replanning that requires repeated CT acquisition and roughly a week of preparation, adding burden, cost, and delay. We investigate whether a patient's treatment-day anatomy can be predicted before image acquisition by transferring longitudinal change from a population database. We propose a digital-twin framework built on a pretrained foundation-model deformable registration network used without patient-specific training. A first registration aligns a prior patient's planning CT to the target and carries the prior's during-treatment quality assurance CT (QACT) into the target frame; a second registration estimates the prior's planning-to-QACT change, which is then applied to the target's own planning CT to synthesize predicted CTs (pdCTs) with propagated contours. Using 88 HN patients, each with a planning CT and three QACTs, we show that pdCTs better match treatment-day anatomy than the static planning CT. Compared with the planning CT alone, normalized cross-correlation improves by 22.8%, Dice for organs-at-risk by 20.2%, and CT-number error decreases by 23.4%. Gains are largest for patients with major anatomical change and negligible when anatomy is stable. This cross-patient motion transfer leverages the digital-twin concept to anticipate treatment-day anatomy, enabling personalized online adaptive proton therapy without repeated imaging.
CommentsAccepted for publication in the Proceedings of the 29th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2026), Workshop on Digital Twins for Healthcare (DT4H)
