Borges, P; Fernandez, V; Nachev, P; Ourselin, S; Cardoso, MJ; (2025) Unsupervised MRI Harmonization via Parameter Prediction and Super-Resolved MPMs. In: Fernandez, V and Wiesner, D and Zuo, L and Casamitjana, A and Remedios, SW, (eds.) Simulation and Synthesis in Medical Imaging. SASHIMI 2025. (pp. pp. 107-116). Springer: Cham, Switzerland.

Text SR_Param_MPMGen_2025 (3).pdf - Accepted Version Access restricted to UCL open access staff until 22 September 2026. Download (3MB)

Abstract

This study presents a significant advancement in unsupervised magnetic resonance (MR) image harmonization through the development of SR-Param-MPMGen, a novel framework that translates clinical MR acquisitions into an invariant, isotropic space. Building upon existing methods for multi-parametric map (MPM) generation, our approach introduces automatic parameter prediction, enabling physics-based simulations to convert qualitative MR images into quantitative MPMs without relying on manual parameter inputs or paired training data. The efficacy of SR-Param-MPMGen is validated through image reconstruction-quality analysis and downstream segmentation tasks, demonstrating statistically significant improvements in healthy tissue segmentation compared to a state-of-the-art style transfer method. These results underscore the potential of our approach to improve data harmonization and analysis in large-scale neuroimaging studies and clinical applications, particularly in scenarios with heterogeneous or missing data.

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