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Proceedings Paper

Inverse GANs for accelerated MRI reconstruction
Author(s): Dominik Narnhofer; Kerstin Hammernik; Florian Knoll; Thomas Pock
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Paper Abstract

State-of-the-art algorithms for accelerated magnetic resonance image (MRI) reconstruction are nowadays dominated by deep learning-based techniques. However, the majority of these methods require the respective sampling patterns in training, which limits their application to a specific problem class. We propose an iterative reconstruction approach that incorporates the implicit prior provided by a generative adversarial network (GAN), which learns the probability distribution of uncorrupted MRI data in an off-line step. Since the unsupervised training of the GAN is completely independent of the measurement process, our method is in principle able to address multiple sampling modalities using a single pre-trained model. However, it turns out that the desired target images potentially lie outside the range space of the learned GAN, leading to reconstructions that resemble the target images only at a coarse level of detail. To overcome this issue, we propose a refinement scheme termed GAN prior adaption, that allows for additional adaption of the generating network with respect to the measured data. The proposed method is evaluated on multi-coil knee MRI data for different acceleration factors and compared to a classical as well as a deep learning-based approach showing promising quantitative and qualitative results.

Paper Details

Date Published: 9 September 2019
PDF: 12 pages
Proc. SPIE 11138, Wavelets and Sparsity XVIII, 111381A (9 September 2019); doi: 10.1117/12.2527753
Show Author Affiliations
Dominik Narnhofer, Graz Univ. of Technology (Austria)
Kerstin Hammernik, Graz Univ. of Technology (United Kingdom)
Imperial College London (United Kingdom)
Florian Knoll, New York Univ. (United States)
Thomas Pock, Graz Univ. of Technology (Austria)

Published in SPIE Proceedings Vol. 11138:
Wavelets and Sparsity XVIII
Dimitri Van De Ville; Manos Papadakis; Yue M. Lu, Editor(s)

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