Unsupervised Heteromodal Physics-Informed Representation of MRI Data: Tackling Data Harmonisation, Imputation and Domain Shift

Abstract

Clinical MR imaging is typically qualitative, i.e. the observed signal reflects the underlying tissue contrast, but the measurements are not meaningful when taken in isolation. Quantitative MR imaging maps are rarely acquired due to time and complexity constraints but directly measure intrinsic tissue properties, allowing for explicit tissue characterisation and MR contrast simulation. A machine learning network trained on quantitative MRI would circumvent the need to design contrast-agnostic models, minimise domain shift issues, and reduce complex data pre-processing. Such models would also be future-proof by design, anticipating new qualitative sequence developments and changes in acquisition parameters. In this work, we propose a new Bloch-equation-based physics-informed unsupervised network that learns to map qualitative MR data to their quantitative equivalents without the need for paired qualitative-quantitative data. Furthermore, we make the proposed model robust to missing data, enabling us to map any arbitrary set of qualitative data from a patient into quantitative Multi-Parametric Maps (MPMs). We demonstrate that the estimated MPMs are a robust and invariant data representation, are self-consistent, enable missing data imputation, and facilitate data harmonisation from multiple sites while bridging algorithmic domain gaps.