| MR图像预测CT图像研究进展 |
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| 引用本文： | 奚谦逸,谢凯,高留刚,孙佳伟,倪昕晔,焦竹青. MR图像预测CT图像研究进展[J]. 中国辐射卫生, 2021, 30(3): 366-370. DOI: 10.13491/j.issn.1004-714X.2021.03.021 |
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| 作者姓名： | 奚谦逸 谢凯 高留刚 孙佳伟 倪昕晔 焦竹青 |
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| 作者单位： | 1. 常州大学微电子与控制工程学院，江苏 常州 213164;2. 南京医科大学附属常州第二人民医院放疗科，江苏 常州 213003;3. 南京医科大学医学物理研究中心，江苏 常州 213003 |
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| 基金项目： | 常州市医学物理重点实验室项目（CM20193005），江苏省卫生健康委面上项目（M2020006），常州市应用基础研究（CJ20200099），常州市卫生健康委青苗人才（CZQM2020075），（CZQM2020067），常州市卫生健康委青年项目（QN201932） |
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| 摘 要： | 医学图像可以为医生提供准确和全面的病患信息.由于人体因各种疾病引起的形态或功能异常可以表现在很多方面,MR图像和CT图像能重点呈现出患者不同组织结构的医学图像数据,但单独的MR图像或者CT图像不能全面反应出疾病的复杂性.MR图像预测CT图像属于医学图像跨模态预测的一种,将MR图像预测CT图像的方法分为4类,基于图集的方...
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| 关 键 词： | 跨模态预测 图集 图像分割 深度学习 |
| 收稿时间： | 2021-01-28 |
Research progress of MR imaging for prediction of CT imaging |
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| XI Qianyi,XIE Kai,GAO Liugang,SUN Jiawei,NI Xinye,JIAO Zhuqing. Research progress of MR imaging for prediction of CT imaging[J]. Chinese Journal of Radiological Health, 2021, 30(3): 366-370. DOI: 10.13491/j.issn.1004-714X.2021.03.021 |
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| Authors: | XI Qianyi XIE Kai GAO Liugang SUN Jiawei NI Xinye JIAO Zhuqing |
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| Affiliation: | 1. School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164 China;2. Department of Radiotherapy the Second People's Hospital of Changzhou Affiliated to Nanjing Medical University, Changzhou 213003 China;3. Central Laboratory of Medical Physics, Nanjing Medical University, Changzhou 213003 China |
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| Abstract: | Medical images can provide clinicans with accurate and comprehensive patients’ information. Morphological or functional abnormalities caused by various diseases can be manifested in many aspects. Although MR images and CT images can highlight the medical image data of different tissue structures of patients, single MR images or CT images cannot fully reflect the complexity of diseases. Using MR image to predict CT image is one of the cross-modal prediction of medical images. In this paper, the methods of MR image prediction for CTmage are classified into four categoriesincluding registration based on atlas, based on image segmentationmethod, based on learning method and based on deep learning method. In our research, we concluded that the method based on deep learning should bemore promoted in the future by compering the existing problems and future development of MR image predicting CT image method. |
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| Keywords: | Cross-modal Prediction Atlas Image Segmentation T Deep Learning |
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