Improving low-dose X-ray CT images by Weighted Intensity Averaging over Large-scale Neighborhoods

Abstract

How to reduce the radiation dose delivered to the patients is always an important concern since the introduction of computed tomography (CT). With respect to patients' care, the least possible radiation dose is demanded. Though clinically desired, low-dose CT (LDCT) images tend to be severely degraded by quantum noise and artifacts under low dose scan protocols. This paper proposes to improve the LDCT images by Weighted Intensity Averaging over Large-scale Neighborhoods (WIA-LN). In the implementation of the proposed WIA-LN method, the processed pixel intensities are from a selective weighted intensity averaging of the pixels belonging to different organs or attenuation tissues within large-scale neighborhoods. Effective suppression of noise and artifacts in LDCT images without obvious loss of fine anatomic features are realized. In experiment, CT images of different doses from a Siemens CT with 16 detector rows are used. Results validate an excellent performance of the proposed approach in improving clinical LDCT images.