With the recent, rapid development of multidetector computed tomography (MDCT), excitement has built around the possibility of noninvasively imaging the coronary arteries. While the development of hardware and reconstruction technologies have advanced significantly, current image analysis techniques are dominated by manual interpretation using maximum intensity projections and volume rendering. If MDCT is to become the tool that it aims to be, objective, quantitative methods of image analysis will be necessary - not only to facilitate the study of atherosclerosis and coronary heart disease, but also for the accurate and timely interpretation of clinical data. This study focuses on the interobserver variability associated with the analysis of coronary MDCT images and a method for automatic segmentation of the same images. In the study of interobserver variability, six independent experts manually traced the luminal border in 60 randomly selected vascular cross sections (5 cross section each from: 4 LAD, 4 LCX, and 4 RCA). The images were acquired with an Mx8000 IDT 16-slice MDCT scanner. The mean unsigned difference for all observers was 0.38 ± 0.26 mm, with an average maximum difference of 1.32 mm. Using the expertly identified luminal borders, an independent standard was created by averaging the six sets of contours. This standard was then used to validate a prototypical automated segmentation system that uses dynamic programming and a knowledge-based cost function to optimally segment the luminal border. The resulting border positioning error was 0.17 ± 0.12 mm.
The objective of this study was to evaluate a new Cardiac Enhancement Filter (CEF) for noise reduction and edge enhancement of Computed Tomography Cardiac Angiography examinations. The filter is an adaptive noise reduction filter designed to achieve near real time functioning. Using a CT performance phantom, standard measurements of image quality, including spatial resolution, low contrast resolution, and image noise were assessed with and without the CEF. Quantitative assessment of the CEF showed slightly improved spatial resolution at 50% and 10% modulation, similar low contrast resolution and significantly lower image noise (up to 38%) characteristics. Two patient datasets were used for the clinical evaluation of the filter. The filter effectively reduced image noise by 13 to 22% in clinical datasets. These datasets exhibited a significant decrease in image noise without loss of vessel sharpness or introduction of new image artifacts. The results of the initial testing are encouraging, yet additional investigations are required to further assess the filter's clinical utility.