Search

Cone beam reconstructed cardiac CT images suffer from characteristic streak artifacts that affect the quality of coronary artery imaging. These artifacts arise from inhomogeneous distribution of noise. While in non-tagged reconstruction inhomogeneity of noise distribution is mainly due to anisotropy of the attenuation of the scanned object (e.g. shoulders), in cardiac imaging it is largely influenced by the non-uniform distribution of the acquired data used for reconstructing the heart at a given phase. We use a cardiac adaptive filter to reduce these streaks. In difference to previous methods of adaptive filtering that locally smooth data points on the basis of their attenuation values, our filter is applied as a function of the noise distribution of the data as it is used in the phase selective reconstruction. We have reconstructed trans-axial images without adaptive filtering, with a regular adaptive filter and with the cardiac adaptive filter. With the cardiac adaptive filter significant reduction of streaks is achieved, and thus image quality is improved. The coronary vessel is much more pronounced in the cardiac adaptive filtered images, in slab MIP the main coronary artery branches are more visible, and non-calcified plaque is better differentiated from vessel wall. This improvement is accomplished without altering significantly the border definition of calcified plaques.

Computed Tomography Angiography (CTA) is an emerging modality for assessing cardiac anatomy. The delineation of the cardiac volume of interest (VOI) is a pre-processing step for subsequent visualization or image processing. It serves the suppression of anatomic structures being not in the primary focus of the cardiac application, such as sternum, ribs, spinal column, descending aorta and pulmonary vasculature. These structures obliterate standard visualizations such as direct volume renderings or maximum intensity projections. In addition, outcome and performance of post-processing steps such as ventricle suppression, coronary artery segmentation or the detection of short and long axes of the heart can be improved. The structures being part of the cardiac VOI (coronary arteries and veins, myocardium, ventricles and atria) differ tremendously in appearance. In addition, there is no clear image feature associated with the contour (or better cut-surface) distinguishing between cardiac VOI and surrounding tissue making the automatic delineation of the cardiac VOI a difficult task. The presented approach locates in a first step chest wall and descending aorta in all image slices giving a rough estimate of the location of the heart. In a second step, a Fourier based active contour approach delineates slice-wise the border of the cardiac VOI. The algorithm has been evaluated on 41 multi-slice CT data-sets including cases with coronary stents and venous and arterial bypasses. The typical processing time amounts to 5-10s on a 1GHz P3 PC.