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

arXiv: https://arxiv.org/abs/2607.00500 · HTML: https://mlsanity.com/paper/2607.00500/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 29 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## Builds on (most cited first, 2 of 2)

- [Distortion-Corrected Image Reconstruction with Deep Learning on an MRI-Linac](https://mlsanity.com/paper/2205.10993/) (2022)
- [On Real-time Image Reconstruction with Neural Networks for MRI-guided Radiotherapy](https://mlsanity.com/paper/2202.05267/) (2022)

## Abstract (arXiv)

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.