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Learning-Based Artifact Removal via Image Decomposition for Low-Dose CT Image Processing

Streak artifacts and mottle noise often appear in low-dose CT (LDCT) images due to excessive quantum noise in low-dose X-ray imaging process, thus degrading CT image quality. This research is aimed at improving the quality of LDCT images via image decomposition and dictionary learning. The proposed method first decomposes a LDCT image into the low-frequency (LF) and high-frequency (HF) parts by a bilateral filter. The HF part is then decomposed into an artifact component and a tissue component by performing dictionary learning (DL) and sparse coding. The tissue component is combined with the LF part to obtain the artifact-suppressed image. At last, a DL method is applied to further reduce the residual artifacts and noise. Different from previous research works with sparse representation, the proposed method does not need to collect training images in advance. The results of numerical simulation and clinical data experiments indicate the effectiveness of the proposed approach.