基于CT图像的跨模态转换研究进展

医学图像在临床诊断和治疗上起着至关重要的作用。放射治疗过程中采用计算机体层成像（CT）进行靶区定位和勾画。为了从多个角度获取病变体信息，需利用医学图像多模态的优势。然而，获取多种模态的医学图像是比较耗费资源的，同时无法保证患者状态的一致性。医学图像跨模态转换，可以利用一种模态图像预测另一种模态图像。本文详细综述了基于CT图像的超声图像、磁共振（magnetic resonance，MR）图像、正电子发射计算机断层显像（positron emission tomography，PET）跨模态模型研究，分类阐述了各模型的特点和存在的挑战，指出尚待开展的研究领域。

Research progress of CT-based multiple modality medical image translation

Medical images play an important role in clinical diagnosis and treatment. During the radiotherapy, CT can be available for the location and definition of the target volume. The medical images from multiple modalities are used to obtain the information on pathological body from many angles. However, obtaining multiple-modality medical images could be more resource-consuming, and difficult to guarantee the consistency of patients'' state. Medical image translation between multiple modalities can achieve the predication from one modality to another. The studies on medical images from multiple modalities such as CT, ultrasound, MRI and PET are reviewed in detail in this paper,, with discussions provided about characteristics of multiple modalities and challenges faced, as well as the research areas to be developed.