Self-correcting MRI: what if seismology held the answer?

The three-phase validation cascade (in silico → physical phantom → in vivo) is exemplary, correctly prioritising internal validity before progressing to external validity, and incorporating pre-defined GO/NO-GO/PIVOT criteria at each phase to guide decision-making.

The formulation is conceptually bold and proposes a rigorous methodological transfer (adjoint method, multi-scale continuation) from a mature domain (seismic FWI) to an emerging domain (multi-contrast quantitative MRI). This interdisciplinary bridge is pertinent and potentially fruitful, as it addresses a genuine need for regularisation and for reducing trade-offs between parameters in quantitative imaging.

The ambition to unify MRI parameter estimation under a single variational framework is intellectually coherent, and the explicit use of adjoint-based gradients is a mathematically sound approach for high-dimensional nonlinear inverse problems.

Identifiable and growing market: quantitative MRI (qMRI) diagnostics for neurodegenerative diseases (Alzheimer, Parkinson, multiple sclerosis) and oncology constitute an expanding segment. The principal manufacturers (Siemens Healthineers, GE Healthcare, Philips) are investing in quantification solutions, and pharmaceutical CROs (ICON, Parexel) are potential clients for more precise biomarkers in clinical trials. The TAM for qMRI analysis software is estimated at between USD 500 million and USD 1 billion by 2030.

Originality and conceptual transfer: The hypothesis explicitly imports the adjoint-based multiscale optimisation paradigm from seismic Full-Waveform Inversion (FWI) into MRI. This is a genuine cross-disciplinary advance that could disrupt the current sequential reconstruction paradigm in quantitative MRI, a strong selling point for novelty-driven programmes such as ERC.