Learning a preconditioner to accelerate compressed sensing reconstructions in MRI

Purpose To learn a preconditioner that accelerates parallel imaging (PI) and compressed sensing (CS) reconstructions. Methods A convolutional neural network (CNN) with residual connections was used to train a preconditioning operator. Training and validation data were simulated using 50% brain image...

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Bibliographic Details
Published in:	Magnetic resonance in medicine Vol. 87; no. 4; pp. 2063 - 2073
Main Authors:	Koolstra, Kirsten, Remis, Rob
Format:	Journal Article
Language:	English
Published:	United States Wiley Subscription Services, Inc 01.04.2022 John Wiley and Sons Inc
Subjects:	Acceleration Algorithms Artificial neural networks Brain - diagnostic imaging compressed sensing Computer architecture Conditioned stimulus deep learning Image Processing, Computer-Assisted - methods Image reconstruction Magnetic resonance imaging Magnetic Resonance Imaging - methods Medical imaging MR image reconstruction Neural networks Neural Networks, Computer Neuroimaging parallel imaging Parameter sensitivity Preconditioning Random noise Regularization Sampling split Bregman Technical Note Technical Notes—Computer Processing and Modeling Training split Bregman deep learning parallel imaging compressed sensing MR image reconstruction preconditioning
ISSN:	0740-3194, 1522-2594, 1522-2594
Online Access:	Get full text
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