Leiden University Scholarly Publications

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Pezzotti, N.; Yousefi, S.; Elmahdy, M.S.; Gemert, J.H.F. van; Schuelke, C.; Doneva, M.; ... ; Staring, M. (2020)

An adaptive intelligence algorithm for under sampled knee MRI reconstruction

Article / Letter to editor
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 8x accelerated multi-coil, the 4x multi-coil, and...Show moreAdaptive 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 8x accelerated multi-coil, the 4x multi-coil, and the 4x single-coil tracks. This demonstrates the superior performance and wide applicability of the method.Show less
Image reconstruction
Magnetic resonance imaging
Acceleration
Coils
Training
Optimization
MRI
deep learning
ISTA
fastMRI challenge
All authors
Pezzotti, N.; Yousefi, S.; Elmahdy, M.S.; Gemert, J.H.F. van; Schuelke, C.; Doneva, M.; Nielsen, T.; Kastryulin, S.; Lelieveldt, B.P.F.; Osch, M.J.P. van; Weerdt, E. de; Staring, M.
Date
2020-01-01
Journal
IEEE Access
Volume
8
Pages
204825 - 204838
DOI
doi:10.1109/ACCESS.2020.3034287
Link
https://ieeexplore.ieee.org/document/9241039