MR图像预测CT图像研究进展  

Research progress of MR imaging for prediction of CT imaging

作　　者：奚谦逸 谢凯 高留刚[2,3] 孙佳伟 倪昕晔[2,3] 焦竹青 XI Qianyi;XIE Kai;GAO Liugang;SUN Jiawei;NI Xinye;JIAO Zhuqing(School of Microelectronics and Control Engineering,Changzhou University,Changzhou 213164 China;Department of Radiotherapy the Second People's Hospital of Changzhou Affiliated to Nanjing Medical University,Changzhou 213003 China;Central Laboratory of Medical Physics,Nanjing Medical University,Changzhou 213003 China)

机构地区：[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

出　　处：《中国辐射卫生》2021年第3期366-370,共5页Chinese Journal of Radiological Health

基　　金：常州市医学物理重点实验室项目(CM20193005);江苏省卫健委面上项目(M2020006);常州市应用基础研究(CJ20200099);常州市卫健委青苗人才(CZQM2020075),(CZQM2020067);常州市卫健委青年项目(QN201932)。

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,基于图集的方法、基于图像分割的方法、基于学习的方法和基于深度学习的方法。本文对MR图像预测CT图像的各类方法、存在问题和未来发展方向进行综述,得出结论基于深度学习的方法应是未来跨模态预测的主要方法。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.

关 键 词：跨模态预测 图集 图像分割 深度学习 

分 类 号：R812[医药卫生—放射医学]