MRI-based synthetic CT generation using deep convolutional neural network

Yang Lei; Tonghe Wang; Yingzi Liu; Kristin Higgins; Sibo Tian; Tian Liu; Hui Mao; Hyunsuk Shim; Walter J. Curran; Hui-Kuo Shu; Xiaofeng Yang

doi:10.1117/12.2512549

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15 March 2019 MRI-based synthetic CT generation using deep convolutional neural network

Yang Lei, Tonghe Wang, Yingzi Liu, Kristin Higgins, Sibo Tian, Tian Liu, Hui Mao, Hyunsuk Shim, Walter J. Curran, Hui-Kuo Shu, Xiaofeng Yang

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Proceedings Volume 10949, Medical Imaging 2019: Image Processing; 109492T (2019) https://doi.org/10.1117/12.2512549
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

We propose a learning method to generate synthetic CT (sCT) image for MRI-only radiation treatment planning. The proposed method integrated a dense-block concept into a cycle-generative adversarial network (cycle-GAN) framework, which is named as dense-cycle-GAN in this study. Compared with GAN, the cycle-GAN includes an inverse transformation between CT (ground truth) and sCT, which could further constrain the learning model. A 2.5D fully convolution neural network (FCN) with dense-block was introduced in generator to enable end-to-end transformation. A FCN is used in discriminator to urge the generator’s sCT to be similar with the ground-truth CT images. The well-trained model was used to generate the sCT of a new MRI. This proposed algorithm was evaluated using 14 patients’ data with both MRI and CT images. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and normalized cross correlation (NCC) indexes were used to quantify the correction accuracy of the prediction algorithm. Overall, the MAE, PSNR and NCC were 60.9−11.7 HU, 24.6±0.9 dB, and 0.96±0.01. We have developed a novel deep learning-based method to generate sCT with a high accuracy. The proposed method makes the sCT comparable to that of the planning CT. With further evaluation and clinical implementation, this method could be a useful tool for MRI-based radiation treatment planning and attenuation correction in a PET/MRI scanner.

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Yang Lei, Tonghe Wang, Yingzi Liu, Kristin Higgins, Sibo Tian, Tian Liu, Hui Mao, Hyunsuk Shim, Walter J. Curran, Hui-Kuo Shu, and Xiaofeng Yang "MRI-based synthetic CT generation using deep convolutional neural network ", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109492T (15 March 2019); https://doi.org/10.1117/12.2512549

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