Directly Filtering the Sparse-View CT Images by BM3D

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 if they are used to reduce the artifacts. 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 clinical 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 using an artifact power spectral density function, which is calculated with computer simulations. The results in this paper show that the proposed method is promising in computer simulations. The proposed method has been applied to patient data, and we observe that the sparse-view artifacts are reduced, especially in the central region of the image, but the artifact reduction is not as effective at the peripheral if the control parameter in the BM3D filter is not properly chosen.