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

• URL: http://arxiv.org/abs/2001.08746v2
• Date: Tue, 21 Apr 2020 20:09:21 GMT
• Title: Compressive MRI quantification using convex spatiotemporal priors and deep auto-encoders
• Authors: Mohammad Golbabaee, Guido Buonincontri, Carolin Pirkl, Marion Menzel, Bjoern Menze, Mike Davies, Pedro Gomez
• Abstract summary: We propose a dictionary-free pipeline for multi-parametric image computing. Our approach has two stages based on compressed sensing reconstruction and learned quantitative inference.
• Score: 2.5060548079588516
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a dictionary-matching-free pipeline for multi-parametric quantitative MRI image computing. Our approach has two stages based on compressed sensing reconstruction and deep learned quantitative inference. The reconstruction phase is convex and incorporates efficient spatiotemporal regularisations within an accelerated iterative shrinkage algorithm. This minimises the under-sampling (aliasing) artefacts from aggressively short scan times. The learned quantitative inference phase is purely trained on physical simulations (Bloch equations) that are flexible for producing rich training samples. We propose a deep and compact auto-encoder network with residual blocks in order to embed Bloch manifold projections through multiscale piecewise affine approximations, and to replace the nonscalable dictionary-matching baseline. Tested on a number of datasets we demonstrate effectiveness of the proposed scheme for recovering accurate and consistent quantitative information from novel and aggressively subsampled 2D/3D quantitative MRI acquisition protocols.

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