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Proceedings Paper

Deep attentional GAN-based high-resolution ultrasound imaging
Author(s): Xiuxiu He; Yang Lei; Yingzi Liu; Zhen Tian; Tonghe Wang; Walter J. Curran; Tian Liu; Xiaofeng Yang
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Paper Abstract

A routine 3D transrectal ultrasound (TRUS) volume is usually captured with large slice thickness (e.g., 2-5mm). Such ultrasound images with low out-of-slice resolution affect contouring and needle/seed detection in prostate brachytherapy. The purpose of this study is to develop a deep-learning-based method to construct high-resolution images from routinely captured prostate ultrasound images for brachytherapy. We propose to integrate a deeply supervised attention model into a Generative Adversarial Network (GAN)-based framework to improve ultrasound 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 highresolution images. This technique was validated with 20 patients. We performed a leave-one-out cross-validation method to evaluate the proposed algorithm. Our reconstructed, high-resolution TRUS images from down-sampled images were compared with the original image to evaluate the performance quantitatively. The mean absolute error (MAE) and peak signal-to-noise ratio (PSNR) of image intensity profiles between reconstructed and original images were 6.5 ± 0.5 and 38.0 ± 2.4dB.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11319, Medical Imaging 2020: Ultrasonic Imaging and Tomography, 113190B (16 March 2020); doi: 10.1117/12.2549556
Show Author Affiliations
Xiuxiu He, Winship Cancer Institute of Emory Univ. (United States)
Yang Lei, Winship Cancer Institute of Emory Univ. (United States)
Yingzi Liu, Winship Cancer Institute of Emory Univ. (United States)
Zhen Tian, Winship Cancer Institute of Emory Univ. (United States)
Tonghe Wang, Winship Cancer Institute of Emory Univ. (United States)
Walter J. Curran, Winship Cancer Institute of Emory Univ. (United States)
Tian Liu, Winship Cancer Institute of Emory Univ. (United States)
Xiaofeng Yang, Winship Cancer Institute of Emory Univ. (United States)


Published in SPIE Proceedings Vol. 11319:
Medical Imaging 2020: Ultrasonic Imaging and Tomography
Brett C. Byram; Nicole V. Ruiter, Editor(s)

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