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

Mohammad Golbabaee, Guido Buonincontri, Carolin M. Pirkl, Marion I. Menzel, Bjoern H. Menze, Mike Davies, Pedro A. Gómez

Research output: Contribution to journalArticlepeer-review

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 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 proposed scheme for recovering accurate and consistent quantitative information from novel and aggressively subsampled 2D/3D quantitative MRI acquisition protocols.

Original languageEnglish
Article number101945
JournalMedical Image Analysis
Volume69
Early online date19 Dec 2020
DOIs
Publication statusE-pub ahead of print - 19 Dec 2020

Keywords

  • Compressed sensing
  • Convex model-based reconstruction
  • Encoder-decoder network
  • Magnetic resonance fingerprinting
  • Residual network

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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