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15 March 2019 MRI-based synthetic CT generation using deep convolutional neural network
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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.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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