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Proceedings Paper

Multi-modality MRI arbitrary transformation using unified generative adversarial networks
Author(s): 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 perform arbitrary image-to-image translations among four types of MRI scans, including T1-weighted, T1c (T1-weighted with contrast enhancement), Flair and T2-weighted. The goal is to rapidly generate different contrast weighted images which provide comprehensive diagnostic information. The proposed method employs a unified generative adversarial network (unified GAN) which translates any randomly selected MRI scan to the rest scan types. Compared to traditional GAN which takes only images as input, the proposed unified GAN takes both the original image and target domain label as input. The proposed method was evaluated using 50 patients’ brain datasets with well-aligned multi-types of MRI scans. Normalized mean absolute error (NMAE) and peak signal-tonoise ratio (PSNR) were used to quantify the synthesis accuracy of the proposed method. With T2 scan as input, the average NMAE was 0.018±0.003, 0.014±0.002, and 0.022±0.005 for T1, T1c and Flair MRI scans, respectively. The average PSNR was 30.1±3.7 dB, 36.3±3.5 dB, and 30.4±4.7 dB for T1, T1c and Flair MRI scans, respectively. Image quality of the synthesized MRI scans are comparable to original MRI scans.

Paper Details

Date Published: 10 March 2020
PDF: 6 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131303 (10 March 2020); doi: 10.1117/12.2549794
Show Author Affiliations
Yang Lei, Winship Cancer Institute, Emory Univ. (United States)
Yabo Fu, Winship Cancer Institute, Emory Univ. (United States)
Hui Mao, Winship Cancer Institute, Emory Univ. (United States)
Walter J. Curran, Winship Cancer Institute, Emory Univ. (United States)
Tian Liu, Winship Cancer Institute, Emory Univ. (United States)
Xiaofeng Yang, Winship Cancer Institute, 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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