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An adaptive intelligence algorithm for undersampled knee MRI reconstruction

Journal Article (2020)
Authors

Nicola Pezzotti (Philips Research)

Sahar Yousefi (Leiden University Medical Center)

Mohamed S. Elmahdy (Leiden University Medical Center)

Jeroen Hendrikus Fransiscus van Gemert (Philips Healthcare Nederland)

Christophe Schuelke (Philips Innovation Services)

Mariya Doneva (Philips Innovation Services)

Tim Nielsen (Philips Innovation Services)

Boudewijn P.F. Lelieveldy (TU Delft - Pattern Recognition and Bioinformatics, Leiden University Medical Center)

M. Staring (TU Delft - Pattern Recognition and Bioinformatics, Leiden University Medical Center)

G.B. Cavadini (External organisation)

Research Group
Pattern Recognition and Bioinformatics
Copyright
© 2020 Nicola Pezzotti, Sahar Yousefi, Mohamed S. Elmahdy, Jeroen Hendrikus Fransiscus van Gemert, Christophe Schuelke, Mariya Doneva, Tim Nielsen, B.P.F. Lelieveldt, M. Staring, More Authors
To reference this document use:
https://doi.org/10.1109/ACCESS.2020.3034287
More Info
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Publication Year
2020
Language
English
Copyright
© 2020 Nicola Pezzotti, Sahar Yousefi, Mohamed S. Elmahdy, Jeroen Hendrikus Fransiscus van Gemert, Christophe Schuelke, Mariya Doneva, Tim Nielsen, B.P.F. Lelieveldt, M. Staring, More Authors
Research Group
Pattern Recognition and Bioinformatics
Volume number
8
Pages (from-to)
204825-204838
DOI:
https://doi.org/10.1109/ACCESS.2020.3034287
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Abstract

Adaptive intelligence aims at empowering machine learning techniques with the additional use of domain knowledge. In this work, we present the application of adaptive intelligence to accelerate MR acquisition. Starting from undersampled k-space data, an iterative learning-based reconstruction scheme inspired by compressed sensing theory is used to reconstruct the images. We developed a novel deep neural network to refine and correct prior reconstruction assumptions given the training data. The network was trained and tested on a knee MRI dataset from the 2019 fastMRI challenge organized by Facebook AI Research and NYU Langone Health. All submissions to the challenge were initially ranked based on similarity with a known groundtruth, after which the top 4 submissions were evaluated radiologically. Our method was evaluated by the fastMRI organizers on an independent challenge dataset. It ranked #1, shared #1, and #3 on respectively the 8× accelerated multi-coil, the 4× multi-coil, and the 4× single-coil tracks. This demonstrates the superior performance and wide applicability of the method.