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Proceedings Paper

Super-resolution magnetic resonance imaging reconstruction using deep attention networks
Author(s): Xiuxiu He; Yang Lei; Yabo Fu; Hui Mao; Walter J. Curran; Tian Liu; Xiaofeng Yang
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Paper Abstract

We propose a deep-learning-based method to reconstruct super-resolution images from routinely captured MRI images. We propose to integrate a deeply supervised attention model into a generative adversarial network (GAN)-based framework to improve MRI image resolution. Deep attention GANs are introduced to enable end-to-end encoding-and-decoding learning. Next, an attention model is used to retrieve the most relevant information from the encoder. The residual network is used to learn the difference between low- and high-resolution images. This technique was validated with 20 patients. We performed a leave-one-out cross-validation method to evaluate the proposed algorithm and further tested it with a down-sampling rate 1/3 and 1/6. Our reconstructed high-resolution MRI images from down-sampling images were compared with the original image to evaluate the performance quantitatively. The reconstructed super-resolution images were compared to a high-resolution reference scan using and the mean absolute error (MAE) and peak signal-to-noise ratio (PSNR) of image intensity profiles. The MAE and PSNR between reconstructed and original images for 1/3 down sampling rate were 3.86±1.53 and 41.95 ± 5.06dB, and 9.45 ± 1.73, 35.05 ± 3.64dB for 1/6 respectively, which demonstrates the accuracy of the proposed method.

Paper Details

Date Published: 10 March 2020
PDF: 6 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113132J (10 March 2020); doi: 10.1117/12.2549604
Show Author Affiliations
Xiuxiu He, Emory Univ. (United States)
Yang Lei, Emory Univ. (United States)
Yabo Fu, Emory Univ. (United States)
Hui Mao, Emory Univ. (United States)
Walter J. Curran, Emory Univ. (United States)
Tian Liu, Emory Univ. (United States)
Xiaofeng Yang, Emory Univ. (United States)


Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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