CT colonography: Does improvedzresolution help computer-aided polyp detection?

Colon polyp detection using smoothed shape operators: preliminary results

Computer-aided detection (CAD) algorithms identify locations in computed tomographic (CT) images of the colon that are most likely to contain polyps. Existing CAD methods treat the CT data as a voxelized, volume image. They estimate a curvature-based feature at the mucosal surface voxels. However, curvature is a smooth notion, while our data are discrete and noisy. As a second order differential quantity, curvature amplifies noise. In this paper, we present the smoothed shape operators method (SSO), which uses a geometry processing approach. We extract a triangle mesh representation of the colon surface, and estimate curvature on this surface using the shape operator. We then smooth the shape operators on the surface iteratively. Throughout, we use techniques explicitly designed for discrete geometry. All our computation occurs on the surface, rather than in the voxel grid. We evaluate our algorithm on patient data and provide free-response receiver-operating characteristic performance analysis over all size ranges of polyps. We also provide confidence intervals for our performance estimates. We compare our performance with the surface normal overlap (SNO) method for the same data. A preliminary evaluation of our method on 35 patients yielded the following results (polyp diameter range; sensitivity; false positives/case): (10mm; 100%; 17.5), (5-10 mm; 89.7%, 21.23), (<5 mm; 59.1%; 23.9) and (overall; 80.3%; 23.9). The evaluation of the SNO method yielded: (10 mm; 75%; 17.5), (5-10 mm; 43.1%; 21.23), (<5 mm; 15.9%; 23.9) and (overall; 38.5%; 23.9).

Application of the noise power spectrum in modern diagnostic MDCT: part II. Noise power spectra and signal to noise

Balancing dose and image quality requires signal-to-noise (SNR) metrics which incorporate both the variance and the spatial frequency characteristics of noise. In this study, the non-prewhitening matched filter SNR metric is calculated for 2 mm slices of a 1 cm diameter sphere under three different conditions: (1) constant pixel standard deviation, (2) constant dose and (3) constant reconstruction filter. For the constant pixel standard deviation condition, an increase of 260% in SNR was found with increasing filter sharpness. For constant dose, the SNR remained level for smooth to medium filters, then declined by up to 55% with increasing filter sharpness. For a constant reconstruction filter, the SNR increased with dose, but not as high as photon statistics would predict. However, when structured noise was removed from the noise power spectrum, the SNR did vary with quanta statistics. These results offer protocol design guidance for low-frequency-dominated objects.

Development of a synthetic phantom for the selection of optimal scanning parameters in CAD-CT colonography

The aim of this paper is to present the development of a synthetic phantom that can be used for the selection of optimal scanning parameters in computed tomography (CT) colonography. In this paper we attempt to evaluate the influence of the main scanning parameters including slice thickness, reconstruction interval, field of view, table speed and radiation dose on the overall performance of a computer aided detection (CAD)-CTC system. From these parameters the radiation dose received a special attention, as the major problem associated with CTC is the patient exposure to significant levels of ionising radiation. To examine the influence of the scanning parameters we performed 51 CT scans where the spread of scanning parameters was divided into seven different protocols. A large number of experimental tests were performed and the results analysed. The results show that automatic polyp detection is feasible even in cases when the CAD-CTC system was applied to low dose CT data acquired with the following protocol: 13 mAs/rotation with collimation of 1.5 mm x 16 mm, slice thickness of 3.0mm, reconstruction interval of 1.5 mm, table speed of 30 mm per rotation. The CT phantom data acquired using this protocol was analysed by an automated CAD-CTC system and the experimental results indicate that our system identified all clinically significant polyps (i.e. larger than 5 mm).

CAD techniques, challenges, and controversies in computed tomographic colonography

Computer-aided diagnosis (CAD) for computed tomographic colonography (CTC) automatically detects the locations of suspicious polyps and masses on CTC and provides radiologists with a second opinion. CAD has the potential to increase radiologists' diagnostic performance in the detection of polyps and masses and to decrease variability of the diagnostic accuracy among readers without significantly increasing the reading time. Technical developments have advanced CAD substantially during the past several years, and a fundamental scheme for the detection of polyps has been established. The most recent CAD systems based on this scheme produce a clinically acceptable high sensitivity and a low false-positive rate. However, CAD for CTC is still under active development, and the technology needs to be improved further. This report describes the expected benefits, the current fundamental scheme, the key techniques used for detection of polyps and masses on CTC, the current detection performance, as well as the pitfalls, challenges, controversies, and the future of CAD.