[1]张新阳,贺鹏博,刘新国,等.基于深度学习单投影的CT断层成像三维重建[J].中国医学物理学杂志,2021,38(10):1223-1228.[doi:DOI:10.3969/j.issn.1005-202X.2021.10.008]
　ZHANG Xinyang,,et al.Deep learning-based 3D reconstruction of CT tomography from a single projection[J].Chinese Journal of Medical Physics,2021,38(10):1223-1228.[doi:DOI:10.3969/j.issn.1005-202X.2021.10.008]
点击复制

基于深度学习单投影的CT断层成像三维重建()
分享到：

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
38卷
期数:
2021年第10期
页码:
1223-1228
栏目:
医学影像物理
出版日期:
2021-10-27

文章信息/Info

Title:
Deep learning-based 3D reconstruction of CT tomography from a single projection
文章编号:
1005-202X（2021）10-1223-06
作者:
张新阳1; 2; 3; 4; 贺鹏博1; 2; 3; 4; 刘新国1; 2; 3; 4; 戴中颖1; 2; 3; 4; 马圆圆1; 2; 3; 4; 申国盛1; 2; 3; 4; 张晖1; 2; 3; 4; 陈卫强1; 2; 3; 4; 李强1; 2; 3; 4
1.中国科学院近代物理研究所， 甘肃 兰州 730000； 2.中国科学院重离子束辐射生物医学重点实验室， 甘肃 兰州 730000； 3.甘肃省重离子束辐射医学应用基础重点实验室， 甘肃 兰州 730000； 4.中国科学院大学核科学与技术学院， 北京 100049
Author(s):
ZHANG Xinyang1;  2;  3;  4;  HE Pengbo1;  2;  3;  4;  LIU Xinguo1;  2;  3;  4;  DAI Zhongying1;  2;  3;  4;  MA Yuanyuan1;  2;  3;  4;  SHEN Guosheng1;  2;  3;  4;  ZHANG Hui1; 2; 3; 4;  CHEN Weiqiang1;  2;  3;  4;  LI Qiang1;  2;  3;  4
1. Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China 2. Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou 730000, China 3. Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Lanzhou 730000, China 4. School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
关键词:
深度学习; CT单视图断层成像; 三维重建; 卷积神经网络; 稀疏投影成像
Keywords:
Keywords: deep learning single-view computed tomography three-dimensional reconstruction convolutional neural network sparse projection imaging
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2021.10.008
文献标志码:
A
摘要:
【摘要】目的：提出一种基于深度学习的计算机断层扫描（CT）单视图断层成像三维（3D）重建方法，在减少数据采集量和降低成像剂量的情况下对不同患者进行CT图像的3D重建。方法：对不同患者的CT图像进行数据增强和模拟生成对应的数字重建放射影像（DRR），并进行数据归一化操作。利用预处理后的数据通过卷积神经网络训练出一个普适于不同患者的神经网络模型。将训练好的神经网络模型部署在测试数据集上，使用平均绝对误差（MAE）、均方根误差（RMSE）、结构相似性（SSIM）和峰值信噪比（PSNR）对重建结果进行评估。结果：定性和定量分析的结果表明，该方法可以使用不同患者的单张2D图像分别重建出质量较高的3D CT图像，MAE、RMSE、SSIM和PSNR分别为0.006、0.079、0.982、38.424 dB。此外，相比特定于单个患者的情况，该方法可以大幅度提高重建速度并节省70%的模型训练时间。结论：构建的神经网络模型可通过不同患者的2D单视图重建出相应患者的3D CT图像。因此，本研究对简化临床成像设备和放射治疗当中的图像引导具有重要作用。
Abstract:
Abstract: Objective To propose a deep learning-based method for the 3D reconstruction of single-view computed tomography (CT) image, thereby realizing the 3D reconstruction of CT image for different patients under the condition of reducing the amount of data collection and decreasing imaging dose. Methods After the CT images of different patients were processed by data enhancement, and the corresponding digitally reconstructed radiograph (DRR) was obtained by simulation, data normalization was carried out. A neural network model universally suitable for different patients was established by training the pre-processed data using deep neural networks, and the trained neural network model was then deployed on the test dataset. Finally, the reconstruction results were evaluated using mean absolute error, root mean square error, structural similarity and peak signal noise ratio. Results The qualitative and quantitative analyses showed that the 3D CT images with high quality could be constructed by the proposed method using a single-view 2D image of different patients, with mean absolute error, root mean square error, structural similarity and peak signal noise ratio of 0.006, 0.079, 0.982 and 38.424 dB, respectively. Additionally, compared with the situation specific to a single patient, the proposed method greatly increased the reconstruction speed and save 70% of model training time. Conclusion The neural network model established in the study can reconstruct the corresponding 3D CT images using single-view 2D image of different patients. The study may play an important role in simplifying clinical imaging devices and image guidance in radiotherapy.

相似文献/References:

[1]陶源,王佳飞,杜俊龙,等.基于卷积神经网络的细胞识别[J].中国医学物理学杂志,2017,34(1):53.[doi:10.3969/j.issn.1005-202X.2017.01.011]
　[J].Chinese Journal of Medical Physics,2017,34(10):53.[doi:10.3969/j.issn.1005-202X.2017.01.011]
[2]门阔,戴建荣. 利用深度反卷积神经网络自动勾画放疗危及器官[J].中国医学物理学杂志,2018,35(3):256.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.002]
　MEN Kuo,DAI Jianrong. Automatic segmentation of organs at risk in radiotherapy using deep deconvolutional neural network[J].Chinese Journal of Medical Physics,2018,35(10):256.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.002]
[3]邓金城,彭应林,刘常春,等. 深度卷积神经网络在放射治疗计划图像分割中的应用[J].中国医学物理学杂志,2018,35(6):621.[doi:DOI:10.3969/j.issn.1005-202X.2018.06.001]
　DENG Jincheng,PENG Yinglin,LIU Changchun,et al. Application of deep convolution neural network in radiotherapy planning image segmentation[J].Chinese Journal of Medical Physics,2018,35(10):621.[doi:DOI:10.3969/j.issn.1005-202X.2018.06.001]
[4]查雪帆,杨丰,吴俣南,等. 结合迁移学习与深度卷积网络的心电分类研究[J].中国医学物理学杂志,2018,35(11):1307.[doi:DOI:10.3969/j.issn.1005-202X.2018.11.013]
　ZHA Xuefan,YANG Feng,WU Yunan,et al. ECG classification based on transfer learning and deep convolution neural network[J].Chinese Journal of Medical Physics,2018,35(10):1307.[doi:DOI:10.3969/j.issn.1005-202X.2018.11.013]
[5]宫进昌,赵尚义,王远军. 基于深度学习的医学图像分割研究进展[J].中国医学物理学杂志,2019,36(4):420.[doi:DOI:10.3969/j.issn.1005-202X.2019.04.010]
　GONG Jinchang,ZHAO Shangyi,WANG Yuanjun.Research progress on deep learning-based medical image segmentation[J].Chinese Journal of Medical Physics,2019,36(10):420.[doi:DOI:10.3969/j.issn.1005-202X.2019.04.010]
[6]安莹,黄能军,杨荣,等. 基于深度学习的心血管疾病风险预测模型[J].中国医学物理学杂志,2019,36(9):1103.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.021]
　AN Ying,HUANG Nengjun,YANG Rong,et al. Deep learning-based model for risk prediction of cardiovascular diseases[J].Chinese Journal of Medical Physics,2019,36(10):1103.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.021]
[7]徐航,随力,张靖雯,等.卷积神经网络在医学图像分割中的研究进展[J].中国医学物理学杂志,2019,36(11):1302.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.011]
　XU Hang,SUI Li,ZHANG Jingwen,et al.Progress on convolutional neural network in medical image segmentation[J].Chinese Journal of Medical Physics,2019,36(10):1302.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.011]
[8]张富利,崔德琪,王秋生,等.基于深度学习和图谱库方法自动勾画肿瘤放疗中危及器官的比较[J].中国医学物理学杂志,2019,36(12):1486.[doi:DOI:10.3969/j.issn.1005-202X.2019.12.024]
　ZHANG Fuli,CUI Deqi,WANG Qiusheng,et al.Comparative study of deep learning- versus Atlas-based auto-segmentation of organs-at-risk in tumor radiotherapy[J].Chinese Journal of Medical Physics,2019,36(10):1486.[doi:DOI:10.3969/j.issn.1005-202X.2019.12.024]
[9]温佳圆,林国钰,张逸文,等.应用深度学习网络实现肾小球滤过膜超微病理图像的语义分割[J].中国医学物理学杂志,2020,37(2):195.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.012]
　WEN Jiayuan,LIN Guoyu,ZHANG Yiwen,et al.Semantic segmentation of ultrastructural pathological images of glomerular filtration membrane using deep learning network[J].Chinese Journal of Medical Physics,2020,37(10):195.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.012]
[10]秦楠楠,薛旭东,吴爱林,等.基于U-net卷积神经网络的宫颈癌临床靶区和危及器官自动勾画的研究[J].中国医学物理学杂志,2020,37(4):524.[doi:DOI:10.3969/j.issn.1005-202X.2020.04.023]
　QIN Nannan,XUE Xudong,WU Ailin,et al.Automatic segmentation of clinical target volumes and organs-at-risk in radiotherapy for cervical cancer using U-net convolutional neural network[J].Chinese Journal of Medical Physics,2020,37(10):524.[doi:DOI:10.3969/j.issn.1005-202X.2020.04.023]

备注/Memo

备注/Memo:
【收稿日期】2021-04-17 【基金项目】国家重点研发计划（2017YFC0107500）；国家自然科学基金（11875299, 61631001） 【作者简介】张新阳，博士研究生，从事离子束治疗技术基础研究，E-mail: zhangxinyang@impcas.ac.cn 【通信作者】李强，研究员，从事重离子治疗相关研究，E-mail: liqiang@impcas.ac.cn
更新日期/Last Update: 2021-10-29