Abstract
Abstract Low-dose computed tomography (CT) image sequences, obtained to reduce the risk of radiation exposure, can be seriously degraded by quantum noise and other kinds of mechanical and electrical effects. In order to overcome this problem, we firstly present a sinogram restoration algorithm based on nonlinear optimization programs (NLP), including sequential quadratic programming (SQP) and the interior point method (IPM). Then, the reconstructed image is obtained by a filtered back-projection (FBP) from smoothed projection data. A real-time scan was provided by applying CUDA, which improves calculation speed and precision. The effectiveness and practicability of the proposed method are validated by utilizing a digital phantom and by real clinical data experiments. The advantage of the proposed method over other techniques is demonstrated by means of the peak signal-noise ratio (PSNR) and Euclidean distance from the original image. The experimental results show that the proposed algorithm has excellent performance for low-dose CT imaging analysis.
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