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

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.