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

13 Citations (SciVal)


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
Early online date19 Dec 2020
Publication statusPublished - 30 Apr 2021

Bibliographical note

Funding Information:
Authors thank Prof. Juan Hernandez-Tamames and Prof. Marion Smits (Radiology and Nuclear Medicine dept, Erasmus MC, University Medical Centre Rotterdam) for providing the patient MRF data which was acquired as part of the work statement B-GEHC-05.

Publisher Copyright:
© 2020

Copyright 2020 Elsevier B.V., All rights reserved.


  • 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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