Super-resolution magnetic resonance imaging reconstruction using deep attention networks

Xiuxiu He; Yang Lei; Yabo Fu; Hui Mao; Walter J. Curran; Tian Liu; Xiaofeng Yang

doi:10.1117/12.2549604

10 March 2020 Super-resolution magnetic resonance imaging reconstruction using deep attention networks

Xiuxiu He, Yang Lei, Yabo Fu, Hui Mao, Walter J. Curran, Tian Liu, Xiaofeng Yang

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 113132J (2020) https://doi.org/10.1117/12.2549604
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

Citation Download Citation

Xiuxiu He, Yang Lei, Yabo Fu, Hui Mao, Walter J. Curran, Tian Liu, and Xiaofeng Yang "Super-resolution magnetic resonance imaging reconstruction using deep attention networks", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113132J (10 March 2020); https://doi.org/10.1117/12.2549604

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