Borges, Pedro;
Fernandez, Virginia;
Tudosiu, Petru Daniel;
Nachev, Parashkev;
Ourselin, Sebastien;
Cardoso, M Jorge;
(2025)
Using MR Physics for Domain Generalisation and Super-Resolution.
In: Fernandez, Virginia and Wolterink, Jelmer M and Wiesner, David and Remedios, Samuel and Zuo, Lianrui and Casamitjana, Adrià, (eds.)
Simulation and Synthesis in Medical Imaging (SASHIMI 2024).
(pp. pp. 177-186).
Springer: Cham, Switzerland.
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Using MR physics for domain generalisation and super-resolution.pdf - Accepted Version Access restricted to UCL open access staff until 7 October 2025. Download (955kB) |
Abstract
MRI is a very flexible imaging modality, but with flexibility comes heterogeneity. MRI sequence choice, acquisition parameters, and image resolution form an extrinsic source of variability, reducing our ability to extract the underlying relevant biological signal and causing difficulties in downstream analyses. We propose a new method that can create resolution and acquisition-parameter invariant representations by removing external sources of variability. We use realistic physics models of image resolution and combine them with a differentiable model of MRI sequences to create an invariant high-resolution multi-parametric (MPM) MRI estimate from an arbitrary number of inputs, all trained via self-supervision. The proposed method allows clinical imaging sessions with sequences acquired at arbitrary resolutions to be transformed into a single-domain generalisable representation. We demonstrate the model’s validity by showing improved MPM reconstruction and imputation quality compared to previous methods and a significantly improved ability to super-resolve. We also demonstrate domain generalisation capabilities via a downstream classification model that is more robust to the choice of input sequences in an out-of-distribution dataset.
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