Research progress of MR imaging for prediction of CT imaging

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 CT image 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.

Abstract

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。