虚拟多模态成像（Virtual multi-Modality Imaging, VMI）是指模拟医学成像设备功能，由源模态图像数据合成特定模态图像的技术，实现医学图像中潜在重要影像信息的提取。高质量的VMI模态合成需获取有效的先验知识，而现有图像先验模型尚不能实现这一目的。鉴于此，本项目提出基于范例图像的医学图像先验（范例先验）模型构造方法，以大规模医学影像数据库为基础建立针对不同模态、不同部位医学图像的非参数先验模型，研究重点包括：（1）设计范例图像搜索和优选策略，实现图像近邻的有效采样；（2）设计层次匹配方法高效求解图像对应场，实现图像块近邻的高效采样；（3）构建结合范例先验的VMI模态合成模型，以期实现优质虚拟CT-MRI成像和虚拟双能减影，用于解决PET/MRI系统中PET图像衰减校正和X线胸片中重叠解剖结构影像分离的问题。

Virtual multi-Modality Imaging (VMI) can synthesize the images of a target modality only using the image data of a source modality by simulating the real imaging devices, and can provide the potential and important visual information of the target modality. However, the existing image prior models can not be effectively applied to solve the highly ill-posed modality synthesis problems of VMI due to the difficulties in learning their model parameters or their limited constraint ability. To obtain sufficient prior knowledge for developing the high-quality VMI technology, this project aims to establish the methods for building the exemplars-based prior models of medical images. Specifically, the non-parametric exemplars-based image prior models for the specific modalities and anatomic sites are built by utilizing image retrieval and image patch matching techniques on the basis of large-scale medical image database. The research priorities of this project include: 1. design of the search and selection strategies of the exemplar images, realizing effective sampling of the image neighbors; 2. design of the hierarchical matching algorithms to obtain the correspondence fields between the medical images, realizing efficient sampling of the image patch neighbors; 3. the modality synthesis models with the exemplars-based priors for virtual CT imaging from MRI image data (vCT-MRI) and virtual Dual Energy Subtraction (vDES). It is to be hoped that the high-quality vCT-MRI and vDES can be achieved through the proposed methods, and can be applied to PET attenuation correction in PET/MRI system and separation of the overlapping anatomical structures in X-ray chest radiographs, respectively.