Directly Filtering the Sparse-View CT Images by BM3D

Abstract

The x-ray computed tomography (CT) images with sparse-view data acquisition contain severe angular aliasing artifacts. The common denoising filters do not work well. The state-of-the-art methods to process the sparse-view CT images are deep learning based; they require a large amount of training data pairs. This paper considers a situation where no training data sets are available. All we have is one sparse scan of a patient. This paper attempts to use a BM3D filter to reduce the artifacts by introducing an artifact power spectral density function, which is calculated with computer simulations. The results in this paper show that the proposed method is not effective enough for practice applications. However, some insights may lead us to further investigations.