SCH: EXP: A Quantitative Platform for CT Colonography

计算机断层扫描结肠镜检查（CTC）是指由放射科医生对准备好的患者进行腹部CT扫描后对结肠表面（管腔）进行可视化。结肠息肉的切除是通过一种称为光学结肠镜检查的微创手术进行的。 CTC早期发现息肉和OC切除息肉之间的适当同步是预防结肠癌的最有效和最经济的方法，早期发现的治愈率超过97%。通过一系列图像分析步骤，可以构建结肠的三维（3D）表示，然后由放射科医生使用虚拟相机进行可视化，以便检查结肠表面的异常和息肉。 可视化包括提取3D结肠表示的中心线，这是虚拟相机的最佳基准。 在这个项目中，提出了一个基于现代计算机视觉和图形学的CTC定量平台，以量化和改善图像分辨率、成像伪影、结肠拓扑结构和息肉位置的可视化过程。在敏感性和特异性方面提高息肉的检测和分类将改善结肠癌的早期检测，这是国家在死亡率和成本方面的主要医疗保健问题。该仿真平台将应用于生物医学教育和培训，以及工业应用中的表面检测和基于图像的可视化隐藏管状物体，除了计算机辅助制造和数字打印。CTC中的可视化包括四个顺序步骤：i）过滤CT扫描以去除扫描仪噪声并解决部分体积效应; ii）分割所得到的图像以将结肠表面与腹部中的其它结构隔离，所述其它结构出现在腹部CT扫描中（例如，肝脏、胰腺和小肠）; iii）使用计算几何学和图形生成3D结肠表示;以及iv）由放射科医师使用虚拟相机来检查管腔表面并检测结肠息肉，从而可视化3D表示。这些步骤中的每一个都基于过去二十年中在图像分析和计算机图形学文献中开发的坚实的数学基础。滤波可以使用各向异性扩散滤波来实现，而分割可以通过统计和变分方法的融合来实现，这将结肠组织与其他解剖结构分离并提供分割的结肠的连续/连接表示。分割的结肠的3D重建通过图形中的常见方法来执行，例如行进立方体算法。可视化是一个复杂的步骤，它涉及到生成3D重建的中心线，以及虚拟相机的适当分配。中心线可以通过变分法和水平集方法生成。可视化中的常见错误是由于成像中的不确定性（例如，扫描仪引起的噪声、部分容积效应、预备残差和患者运动）以及可能导致结肠重建失真或断开的患者特定情况。这些错误可能会掩盖息肉的位置，形状和纹理，导致误诊。本项目旨在开发一种新的CTC前端仿真，为虚拟摄像机提供精确的传感器规划。所提出的模拟将提供对CTC的独特理解，并将导致发现图像引导干预的方法，以通过光学结肠镜和结肠切除术的手术计划有效地去除结肠息肉。该模拟平台将适用于生物医学教育，住院放射科医生和医疗保健专业人员的培训，并将促进生物医学领域以外的各种应用。

Computed-Tomography Colonoscopy (CTC) refers to visualization of the colon surface (lumen) by radiologists following an abdominal CT scan of prepped patients. Removal of colonic polyps is performed by a minimally-invasive procedure known as Optical Colonoscopy. A proper synchronization between CTC for early detection of polyps and OC for their removal is the most effective and economical approach to prevent colon cancer, which has over 97% rate of recovery with early detection. Through a sequence of image analysis steps, a three-dimensional (3D) representation of the colon can be constructed, which is then visualized by radiologists, using a virtual camera, in order to examine the colon surface for abnormalities and polyps. Visualization involves extraction of the centerline of the 3D colon representation, which is the optimum datum for the virtual camera. In this project, a quantitative platform for CTC, based on modern computer vision and graphics, is proposed to quantify and improve the visualization process with respect to image resolution, imaging artifacts, colon topology and polyps' locations. Enhancing the detection and classification of polyps in terms of sensitivity and specificity will improve early detection of colon cancer, a major national healthcare concern in terms of mortality and cost. The proposed simulation platform will have applications in biomedical education and training, and in industrial applications for surface inspection and image-based visualization of hidden tubular objects, in addition to computer-aided manufacturing and digital printing.Visualization in CTC consist of four sequential steps: i) filtering the CT scan for removal of scanner noise and resolving partial volume effects; ii) segmentation of the resulting images to isolate the colon surface from other structures in the abdomen, which appear in the abdominal CT scan (e.g., the liver, pancreas and small intestines); iii) generation of a 3D colon representation using computational geometry and graphics; and iv) visualization of the 3D representation, by radiologists, using virtual cameras to examine the lumen surface and detect colonic polyps. Each of these steps are based on solid mathematical foundation developed in the image analysis and computer graphics literature in the past two decades. Filtering may be achieved using anisotropic diffusion filtering, whereas segmentation can be achieved by fusion of statistical and variational methods, which separates the colon tissues from the other anatomies and provides a continuous/connected representation of the segmented colon. 3D reconstruction of the segmented colon is performed by common methodologies in graphics such the marching-cube algorithm. Visualization is an elaborate step, which involves generating the centerline of the 3D reconstruction, and a proper allocation of the virtual camera. The centerline may be generated by variational calculus and the level sets method. Common errors in visualization are due to the uncertainties in imaging (e.g., scanner-induced noise, partial volume effects, prep residuals and patient motion) and patient-specific circumstances which may lead to distorted or disconnected colon reconstructions. These errors may obscure the location, shape and texture of polyps, leading to erroneous diagnosis. This project aims at developing a novel simulation for the front-end of CTC, an accurate sensor planning for the virtual cameras. The proposed simulation will provide unique understanding of CTC and will lead to discovery of methods for image-guided interventions to efficiently remove colonic polyps by Optical Colonoscopy and surgical planning for colectomy. The simulation platform will be applicable in biomedical education, training of resident radiologists and healthcare professionals, and will also promote various applications outside the biomedical domain.