| Wang Jiangtao,Wu Xinhong,Yan Bing,Zhu Lei,Yang Yidong.Mutual information-based contrastive learning for the generation of pseudo-CT images of the head from magnetic resonance imaging[J].Chinese Journal of Radiological Medicine and Protection,2022,42(2):95-102 |
| Mutual information-based contrastive learning for the generation of pseudo-CT images of the head from magnetic resonance imaging |
| Received:August 10, 2021 |
| DOI：10.3760/cma.j.cn112271-20210810-00318 |
| KeyWords:Magnetic resonance imaging Contrastive learning Pseudo-CT |
| FundProject:中央高校基本科研业务费专项资金（WK2030000037、WK2030040089）；安徽省科技重大专项（BJ2030480006）；国家自然科学基金（81671681）；科技部重点研发计划项目（2016YFC0101400） |
| Author Name | Affiliation | E-mail | | Wang Jiangtao | Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei 230026, China | | | Wu Xinhong | Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei 230026, China | | | Yan Bing | Department of Radiation Oncology, First Affiliated Hospital of USTC, Hefei 230001, China | | | Zhu Lei | Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei 230026, China | | | Yang Yidong | Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei 230026, China | ydyang@ustc.edu.cn |
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| Abstract:: |
| Objective To compare the abilities of different neural networks to generate pseudo-computed tomography (CT) images from magnetic resonance imaging (MRI) images and to explore the feasibility of pseudo-CT for clinical radiotherapy planning. Methods A total of 29 brain cancer patients with planning CT and diagnostic MRI were selected. 23 of these patients were used for training neural networks and 6 for testing pseudo-CT images. Cycle-consistent generative adversarial network (cycleGAN), contrastive learning for unpaired image-to-image translation (CUT), and improved network denseCUT proposed in this study were applied to generate pseudo-CT images from MRI images. The pseudo-CT images were imported into a clinical treatment planning system to verify the feasibility of applying this method to radiotherapy planning. Results The comparison between the generated pseudo-CT images and real CT images showed that the mean absolute errors were (72.0±6.9),(72.5±8.0), and (64.6±7.3) HU for the cycleGAN, CUT, and denseCUT, respectively. Meanwhile, the structure similarity indices were 0.91±0.01, 0.91±0.01, and 0.93±0.01, respectively. The peak signal-to-noise ratios were (28.5±0.7), (28.5±0.7), and (29.5±0.7) dB, respectively. The 2%/2 mm γ passing rates were 98.05%, 97.92%, and 98.31% for the cycleGAN, CUT, and denseCUT, respectively. Conclusions DenseCUT can generate more accurate pseudo-CT images and the pseudo-CT can meet the demand for the dose calculation of IMRT plan. |
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