SNR-weighted sinogram smoothing with improved noise-resolution properties for low-dose x-ray computed tomography

To treat the noise in low-dose x-ray CT projection data more accurately, analysis of the noise properties of the data and development of a corresponding efficient noise treatment method are two major problems to be addressed. In order to obtain an accurate and realistic model to describe the x-ray CT system, we acquired thousands of repeated measurements on different phantoms at several fixed scan angles by a GE high-speed multi-slice spiral CT scanner. The collected data were calibrated and log-transformed by the sophisticated system software, which converts the detected photon energy into sinogram data that satisfies the Radon transform. From the analysis of these experimental data, a nonlinear relation between mean and variance for each datum of the sinogram was obtained. In this paper, we integrated this nonlinear relation into a penalized likelihood statistical framework for a SNR (signal-to-noise ratio) adaptive smoothing of noise in the sinogram. After the proposed preprocessing, the sinograms were reconstructed with unapodized FBP (filtered backprojection) method. The resulted images were evaluated quantitatively, in terms of noise uniformity and noise-resolution tradeoff, with comparison to other noise smoothing methods such as Hanning filter and Butterworth filter at different cutoff frequencies. Significant improvement on noise and resolution tradeoff and noise property was demonstrated.

为了更准确地处理低剂量X射线CT投影数据中的噪声，对数据噪声特性的分析以及开发相应有效的噪声处理方法是需要解决的两个主要问题。为了获得一个准确且符合实际的模型来描述X射线CT系统，我们使用通用电气高速多层螺旋CT扫描仪在几个固定扫描角度对不同的体模进行了数千次重复测量。所收集的数据通过精密的系统软件进行校准和对数变换，该软件将检测到的光子能量转换为满足拉东变换的正弦图数据。通过对这些实验数据的分析，得到了正弦图每个数据的均值和方差之间的非线性关系。在本文中，我们将这种非线性关系整合到一个惩罚似然统计框架中，用于对正弦图中的噪声进行信噪比自适应平滑。经过所提出的预处理后，正弦图使用未加窗滤波反投影方法进行重建。通过与其他噪声平滑方法（如不同截止频率的汉宁滤波器和巴特沃斯滤波器）进行比较，从噪声均匀性和噪声 - 分辨率权衡方面对所得图像进行了定量评估。结果表明，在噪声 - 分辨率权衡和噪声特性方面有显著改善。