Primary uncleansed 2D versus primary electronically cleansed 3D in limited bowel preparation CT-colonography. Is there a difference for novices and experienced readers?

The purpose of this study was to compare a primary uncleansed 2D and a primary electronically cleansed 3D reading strategy in CTC in limited prepped patients. Seventy-two patients received a low-fibre diet with oral iodine before CT-colonography. Six novices and two experienced observers reviewed both cleansed and uncleansed examinations in randomized order. Mean per-polyp sensitivity was compared between the methods by using generalized estimating equations. Mean per-patient sensitivity, and specificity were compared using the McNemar test. Results were stratified for experience (experienced observers versus novice observers). Mean per-polyp sensitivity for polyps 6 mm or larger was significantly higher for novices using cleansed 3D (65%; 95%CI 57–73%) compared with uncleansed 2D (51%; 95%CI 44–59%). For experienced observers there was no significant difference. Mean per-patient sensitivity for polyps 6 mm or larger was significantly higher for novices as well: respectively 75% (95%CI 70–80%) versus 64% (95%CI 59–70%). For experienced observers there was no statistically significant difference. Specificity for both novices and experienced observers was not significantly different. For novices primary electronically cleansed 3D is better for polyp detection than primary uncleansed 2D

Computed cleansing for virtual colonoscopy using a three-material transition model

Abstract. Virtual colonoscopy is a non-invasive technique for the detection of polyps. Currently, a clean colon is required; as without cleansing the colonic wall cannot be segmented. Enhanced bowel preparation schemes opacify intraluminal remains to enable colon segmentation. Computed cleansing (as opposed to physical cleansing of the bowels) allows removal of tagged intraluminal remains. This paper describes a model that allows proper classification of transitions between three materials: gas, tissue and tagged intraluminal remains. The computed cleansing effectively detects and removes the remains from the data. Inspection of the 'clean ' wall is possible using common surface visualization techniques

Classifying CT Image Data Into Material Fractions by a Scale and Rotation Invariant Edge Model

Abstract—A fully automated method is presented to classify 3-D CT data into material fractions. An analytical scale-invariant description relating the data value to derivatives around Gaussian blurred step edges—arch model—is applied to uniquely combine robustness to noise, global signal fluctuations, anisotropic scale, noncubic voxels, and ease of use via a straightforward segmentation of 3-D CT images through material fractions. Projection of noisy data value and derivatives onto the arch yields a robust alternative to the standard computed Gaussian derivatives. This results in a superior precision of the method. The arch-model parameters are derived from a small, but over-determined, set of measurements (data values and derivatives) along a path following the gradient uphill and downhill starting at an edge voxel. The model is first used to identify the expected values of the two pure materials (named and) and thereby classify the boundary. Second, the model is used to approximate the underlying noisefree material fractions for each noisy measurement. An iso-surface of constant material fraction accurately delineates the material boundary in the presence of noise and global signal fluctuations. This approach enables straightforward segmentation of 3-D CT images into objects of interest for computer-aided diagnosis and offers an easy tool for the design of otherwise complicated transfer functions in high-quality visualizations. The method is applied to segment a tooth volume for visualization and digital cleansing for virtual colonoscopy. Index Terms—Anisotropic Gaussian point spread function (PSF), object segmentation, partial volume effect (PVE), transfer function for visualization, voxel classification. I

Improved Visualization in Virtual Colonoscopy Using Image-Based Rendering

Virtual colonoscopy (VC) is a patient-friendly alternative for colorectal endoscopic examination. We explore visualization aspects of VC such as surface in view, navigation and communication of a diagnosis. A series of unfolded cubes presents an animated full 360-degree omnidirectional field-of-view to the physician, to facilitate thorough and rapid inspection. For communication between physicians a tool has been designed that uses image-based rendering. Clinical evaluation has shown a reduction in inspection time from 28 minutes to 12 minutes without loss of sensitivity. With current virtual colonoscopy using a 2-sided view only 94% of the surface is available for exploration. In our approach the surface in view is increased to potentially 100%. Thus, the entire colon can be explored with better confidence that no regions are missed.