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We propose a dictionary-matching-free pipeline for multi-parametricquantitative MRI image computing. Our approach has two stages based oncompressed sensing reconstruction and deep learned quantitative inference. Thereconstruction phase is convex and incorporates efficient spatiotemporalregularisations within an accelerated iterative shrinkage algorithm. Thisminimises the under-sampling (aliasing) artefacts from aggressively short scantimes. The learned quantitative inference phase is purely trained on physicalsimulations (Bloch equations) that are flexible for producing rich trainingsamples. We propose a deep and compact auto-encoder network with residualblocks in order to embed Bloch manifold projections through multiscalepiecewise affine approximations, and to replace the nonscalabledictionary-matching baseline. Tested on a number of datasets we demonstrateeffectiveness of the proposed scheme for recovering accurate and consistentquantitative information from novel and aggressively subsampled 2D/3Dquantitative MRI acquisition protocols.

Compressive MRI quantification using convex spatiotemporal priors and deep auto-encoders