Separate CT-reconstruction for 3D wavelet based noise reduction using correlation analysis

The projection data measured in computed tomography (CT) and, consequently, the volumes reconstructed from these data contain noise. For a reliable diagnosis and subsequent image processing, like segmentation, the ratio between relevant tissue contrasts and the noise amplitude must be sufficiently large. We propose a novel 3D wavelet based method for structure- preserving noise reduction in CT. By separate reconstructions from disjoint subsets of projections, two volumes can be computed, which only differ with respect to noise. Two disjoint subsets of projections can be directly acquired using a dual- source CT-scanner. Otherwise, the two volumes can be generated by reconstructing even and odd numbered projections separately. Correlation analysis between the approximation coefficients of the two input datasets, combined with an orientation and position dependent noise estimation are used for differentiating between structure and noise at each level of the wavelet decomposition. The proposed method adapts itself to the locally varying noise power and allows an anisotropic denoising. The quantitative and qualitative evaluation on phantom and clinical data showed that noise reduction rates up to 60% can be achieved without noticeable loss of resolution.