{"ID":5551876,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-04T06:25:51.571775532Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.00500","arxiv_id":"2607.00500","title":"Closed-loop coupling of personalised and foundation models for real-time treatment guidance with MRI","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2607.00500","url_pdf":"https://arxiv.org/pdf/2607.00500v1","authors":"[\"James Grover\",\"Emily A. Hewson\",\"Andrew Phair\",\"Michael Ferraro\",\"Hilary L. Byrne\",\"Paul Keall\",\"Michael G. Jameson\",\"David E. J. Waddington\"]","published":"2026-07-01T06:32:10Z","proceeding":"physics.med-ph","tasks":"[\"physics.med-ph\",\"cs.CV\"]","methods":"[]","has_code":false}