Multi-modality MRI arbitrary transformation using unified generative adversarial networks

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

doi:10.1117/12.2549794

10 March 2020 Multi-modality MRI arbitrary transformation using unified generative adversarial networks

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

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 1131303 (2020) https://doi.org/10.1117/12.2549794
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

Conference Presentation

Citation Download Citation

Yang Lei, Yabo Fu, Hui Mao, Walter J. Curran, Tian Liu, and Xiaofeng Yang "Multi-modality MRI arbitrary transformation using unified generative adversarial networks", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131303 (10 March 2020); https://doi.org/10.1117/12.2549794

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