| Ming Xin,Yang Chengwen,Meng Huipeng,Zhai Hezheng,Cheng Yuxiang,Yang Miaolong.Study on generation of high energy images from low energy CBCT images based on U-Net model[J].Chinese Journal of Radiological Medicine and Protection,2023,43(9):741-746 |
| Study on generation of high energy images from low energy CBCT images based on U-Net model |
| Received:March 17, 2023 |
| DOI：10.3760/cma.j.cn112271-20230317-00081 |
| KeyWords:Cone bean computed tomography Deep learning Dual-energy imaging Image analysis |
| FundProject:国家自然科学基金（11805144） |
| Author Name | Affiliation | | Ming Xin | School of Biomedical Engineering, Tianjin Medical University, Tianjin 300070, China | | Yang Chengwen | Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China | | Meng Huipeng | Department of Radiation Oncology, Tianjin First Central Hospital, Tianjin 300192, China | | Zhai Hezheng | Institute of Radiation Medicine, Chinese Academy of Medical Sciences, Tianjin 300192, China | | Cheng Yuxiang | School of Biomedical Engineering, Tianjin Medical University, Tianjin 300070, China | | Yang Miaolong | School of Biomedical Engineering, Tianjin Medical University, Tianjin 300070, China |
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| Abstract:: |
| Objective To investigate the conversion of low-energy CBCT images into high-energy CBCT images in clinical radiotherapy based on the deep learning method of U-Net network, in order to provide dual-energy CBCT images and reduce radiation dose.Methods The CBCT image data of CIRS electron density phantom and CIRS head phantom at 80 and 140 kV were collected by the on-board CBCT in radiotherapy equipment. The dataset was divided into training set and test set according to 10:1. The U-Net network was used to predict CBCT images at high energy (140 kV) from low-energy (80 kV) CBCT images. Four parameters, including mean absolute error (MAE), structural similarity index (SSIM), signal-to-noise ratio (SNR) and peak signal-to-noise ratio (PSNR) were used to quantitatively evaluate predicted high-energy CBCT images.Results The overall structural difference between the predicted high-energy image and the real high-energy image was smaller (SSIM:0.993 ±0.003). The noise of predicted high-energy image was lower (SNR:15.33±4.06), but there was a loss of inter-tissue resolution. Predicted high-energy images had slightly lower average CT values than real high-energy images, with less difference in low-density tissues (<10 HU, P > 0.05) and greater differences in high-density tissues (<21 HU, t=-7.92, P < 0.05).Conclusions High-energy CBCT images with high structural similarity can be obtained from energy CBCT images by using deep learning method. The predicted high energy CBCT images have the potential to be applied to clinical dual-energy CBCT imaging technology in radiotherapy. |
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