Analytic Computed Tomography Reconstruction in Sparse-Angular Sampling Using a Sinogram-Normalization Interpolation Method

Abstract

This study investigated analytic computed tomography (CT) reconstruction in sparse-angular sampling using a new sinogram interpolation method to reduce patient radiation dosage. In sparse-angle CT, only a small number of projections, far less than what is required by the Nyquist sampling theory, are taken from the CT system and used for image reconstruction. However, CT images reconstructed by using the standard filtered-backprojection algorithm usually suffer from severe streak artifacts due to theoretically insufficient angular sampling. In this study, a new sinogram interpolation method, the so-called sinogram-normalization interpolation, was introduced to the analytic sparse-angle CT reconstruction to alleviate such artifacts. To validate the proposed method, we performed a systematic simulation and experiment and investigated the image characteristics. CT images were reconstructed using the three sparse-angular samplings of 100, 120, and 150, and their image qualities were quantitatively evaluated in terms of the intensity profile, the peak signal-tonoise ratio, and the universal quality index. The results indicated that the proposed interpolation method effectively reduced streak artifacts in the analytic sparse-angle CT reconstruction, thus maintaining the image quality.