Compressive MRI quantification using convex spatiotemporal priors and deep encoder-decoder networks

Mohammad Golbabaee, Guido Buonincontri, Carolin Pirkl, marion menzel, Bjoern Menze, Michael E. Davies, Pedro Gomez

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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 encoder-decoder network with residual blocks in order to embed Bloch manifold projections through multi-scale piecewise affine approximations, and to replace the non-scalable dictionary-matching baseline. Tested on a number of datasets we demonstrate effectiveness of the
Original languageEnglish
Pages (from-to)101945
JournalMedical Image Analysis
Early online date19 Dec 2020
Publication statusPublished - 1 Apr 2021

Keywords / Materials (for Non-textual outputs)

  • magnetic resonance fingerprinting
  • Compressed sensing
  • Convex model-based reconstruction
  • residual network
  • Encoder-decoder network


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