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


Golbabaee, Mohammad; Buonincontri, Guido; Pirkl, Carolin M; Menzel, Marion I; Menze, Bjoern H; Davies, Mike; Gómez, Pedro A (2021). Compressive MRI quantification using convex spatiotemporal priors and deep encoder-decoder networks. Medical Image Analysis, 69:101945.

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.

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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Department of Quantitative Biomedicine
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Health Sciences > Radiological and Ultrasound Technology
Health Sciences > Radiology, Nuclear Medicine and Imaging
Physical Sciences > Computer Vision and Pattern Recognition
Health Sciences > Health Informatics
Physical Sciences > Computer Graphics and Computer-Aided Design
Uncontrolled Keywords:Radiological and Ultrasound Technology, Health Informatics, Radiology Nuclear Medicine and imaging, Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design
Language:English
Date:1 April 2021
Deposited On:29 Jan 2021 15:34
Last Modified:30 Jan 2021 21:01
Publisher:Elsevier
ISSN:1361-8415
OA Status:Closed
Publisher DOI:https://doi.org/10.1016/j.media.2020.101945
PubMed ID:33421921

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