SCH: EXP: A Quantitative Platform for CT Colonography

University Of Louisville Research Foundation Inc, Louisville KY

Investigators

Abstract

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.