Thoracic Low-dose CT Image Processing Using an Artifact Suppressed Large-scale Nonlocal Means

Abstract: The x-ray exposure to patients has become a major concern in Computed Tomography (CT) and minimizing the radiation exposure has been one of the major efforts in CT field. Due to the plenty high-attenuation tissues in human chest, under low dose scan protocols, thoracic low-dose CT (LDCT) images tend to be severely degraded by excessive mottled noise and non-stationary streak artifacts. Their removal is rather a challenging task because the streak artifacts with directional prominence are often hard to be well discriminated from the attenuation information of normal tissues. This paper describes a two-step processing scheme called “Artifact Suppressed Large-scale Nonlocal Means ” (AS-LNLM) for suppressing both noise and artifacts in thoracic LDCT images. Specific scale and direction properties were exploited to discriminate the noise and artifacts from image structures. Parallel implementation has been introduced to speed up the whole processing by more than 100 times. Phantom and patient CT images were both acquired for evaluation purpose. Comparative qualitative and quantitative analyses were both performed that allows concluding on the efficacy of our method in improving thoracic LDCT data