作为前景可观的管腔内部肿瘤/病变检查方法，基于影像的虚拟内窥镜技术（VE）近年来得到快速发展并已在临床得到应用，但要成为临床的有效诊断手段，还面临着辐射量较大、小息肉及平坦型病变的敏感度低、医生对图像的解释诊断结果差异大/效率低、用于诊断的信息不足等问题和挑战。本项目通过建立VE系统的新框架，将可视化形态学信息与影像数据分析相结合，选取VE极富前景的临床应用领域，以虚拟结肠镜、虚拟膀胱镜、虚拟血管内窥镜为研究对象，基于CT和MRI影像数据，对虚拟内窥镜的共性关键技术，包括基于成像模型的数据优化、管腔内/外壁分割方法、基于形态及纹理特征的计算机辅助检测和诊断技术、基于特征的可视化技术等，进行深入研究，以提高VE系统对小息肉及平坦型病变的检测能力，实现肿瘤浸润深度、息肉/肿瘤分型、斑块成分的计算机辅助分析和诊断，从而极大提高VE系统的检测性能，促进VE成为腔内肿瘤及心血管疾病的有效诊断手段。

Virtual endoscopy (VE) has many advantages over the existing screening options and shows potential as a valuable noninvasive diagnosis tool. There are several challenges remaining for VE to become a diagnosis and/or screening option, such as the concern on associated radiation, low sensitivty on the detection of small polyps and flat lesions, large variation on image intepretation and intepretation efficiency, and limited morphological information of inner surface used for diagnosis. These drawbacks should be addressed before it could become a valuable screening modality. In this study, based on the establishemnt of new VE framework, we aims to develop common key technologies for CT-based virtual colonoscopy, MRI-based virtual cystoscopy and ehanced CT-based virtual intravascular endoscopy. With the establishment of patients' image database, fully-automated image segmentation mitigating the partial volume effect will be developed to extract inner and outer boundaries of hollow organs. Based on the extracted organ wall, geometrical and textural features that demosntrate significant defference between cancerous tissue and normal wall tissue could be extracted and analyzed to detect early sign of abnormalities by either three-dimensional endoscopic views (human observer) or computer-aided detection (computer observer). In addition, Further improvement on computer-aided diagnosis will be expected for the detection of bladder cancer and its invasion, for polyps types, and for component and risk ananlysis of vulnerable plaques based on extracted morphological and texture information. and classified for accurate identification of suspicious patches in the mucosa layer. With the proposed new VE framework and key technologies, the performance of VE systems will be greatly improved, accompanying with simplified procedure for patient preparation and reduced interaction time, for non-invasive detection and screening of high-risk population.