arXiv 2607.005002026-07-02physics.med-phcs.CV

个性化模型与基础模型的闭环耦合用于MRI实时治疗引导

Closed-loop coupling of personalised and foundation models for real-time treatment guidance with MRI

Image X Institute, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia（悉尼大学医学与健康学院悉尼健康科学学院Image X研究所，悉尼，澳大利亚）；GenesisCare, Sydney, Australia（GenesisCare，悉尼，澳大利亚）；School of Clinical Medicine, Medicine & Health, University of New South Wales, Sydney, Australia（新南威尔士大学医学与健康学院临床医学系，悉尼，澳大利亚）；Centre for Medical Radiation Physics, School of Mathematics and Physics, University of Wollongong, Wollongong, Australia（伍伦贡大学数学与物理学院医学辐射物理中心，伍伦贡，澳大利亚）

James Grover, Emily A. Hewson, Andrew Phair, Michael Ferraro, Hilary L. Byrne, Paul Keall, Michael G. Jameson, David E. J. Waddington

备注：: 18 pages, 8 figures, 2 supplementary figures

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AI总结：

提出一种闭环耦合框架，结合个性化时间预测模型与基础模型的连续分割解剖解释，实时补偿MRI引导治疗中的成像延迟，在400毫秒预测范围内改善解剖预测并减少剂量误差。

摘要：

图像引导治疗，包括放射治疗、活检和深部脑刺激，依赖于实时定位解剖结构。然而，在存在运动的情况下，成像延迟会导致观察到的解剖结构与真实解剖结构之间存在时间错位，从而影响治疗精度。基于人工智能的框架越来越多地被提出以缩小这一延迟差距，但领先的个性化模型可能因缺乏稳定的解剖基础而失败。基础模型可以提供基于基础的行为，但它们不能适应实时的个体患者动态。在这里，我们引入了一种闭环耦合框架，将患者特定的时间预测与来自基础模型的连续分割解剖解释协同起来。个性化模型预测未来的解剖结构以补偿系统延迟，而流式基础模型提供解剖监督，用于在治疗期间实时连续更新时间预测器。我们使用数字体模和接受MRI引导放疗的患者的治疗期间磁共振成像（MRI）验证了该框架。对于400毫秒的预测范围，与现有方法相比，所提出的方法在临床相关的延迟约束内改善了解剖预测并减少了剂量误差。这些结果确立了闭环耦合作为实时图像引导干预的一般策略。

英文摘要：

Image-guided therapies, including radiotherapy, biopsy and deep brain stimulation, rely on real-time targeting of anatomical structures. However, in the presence of motion, imaging latencies create a temporal misalignment between observed and true anatomy, compromising treatment accuracy. Artificial intelligence-based frameworks have increasingly been presented to close this latency gap, but leading personalised models can fail due to a lack of stable anatomical grounding. Foundation models can provide grounded behaviour, but they do not adapt to real-time, individual patient dynamics. Here we introduce a closed-loop coupling framework that synergises patient-specific temporal prediction with continuous segmentation-based anatomical interpretation from a foundation model. A personalised model predicts future anatomy to compensate for system latency, while a streaming foundation model provides anatomical supervision used to continuously update the temporal predictor in real time during treatment. We validate the framework using a digital phantom and intrafraction magnetic resonance imaging (MRI) from patients undergoing MRI-guided radiotherapy. For a prediction horizon of 400 ms, the proposed method improves anatomical prediction and reduces dosimetric error compared with existing approaches, within clinically relevant latency constraints. These results establish closed-loop coupling as a general strategy for real-time image-guided intervention.

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