Wang Jing, Liang Zhengrong, Lu Hongbing
Dept. of Radiol., State Univ. of New York, Stony Brook, NY 11794, USA.
Conf Proc IEEE Eng Med Biol Soc. 2006;2006:3282-5. doi: 10.1109/IEMBS.2006.260669.
We propose a novel multiscale penalized weighted least-squares (PWLS) method for restoration of low-dose computed tomography (CT) sinogram. The method utilizes wavelet transform for the multiscale or multi-resolution analysis on the sinogram. Specifically the Mallat-Zhong's wavelet transform is applied to decompose the sinogram to different resolution levels. At each decomposed resolution level, a PWLS criterion is applied to restore the noise-contaminated wavelet coefficients, where the penalty is adaptive to each resolution scale and the weight is adaptive to each scale and each location. The proposed PWLS method is based on the observation that (1) the noisy sinogram of low-dose CT after logarithm transform can be modeled as signal-dependent Gaussian variables and the sample variance depends on the sample mean; and (2) the noise restoration can be more effective when it is adaptive to different resolution levels. The effectiveness of the proposed multiscale PWLS method is validated by an experimental study. The gain by multiscale approach over single-scale means is quantified by noise-resolution tradeoff measures.
我们提出了一种用于低剂量计算机断层扫描(CT)正弦图恢复的新型多尺度惩罚加权最小二乘(PWLS)方法。该方法利用小波变换对正弦图进行多尺度或多分辨率分析。具体而言,应用Mallat-Zhong小波变换将正弦图分解到不同的分辨率级别。在每个分解的分辨率级别上,应用PWLS准则来恢复受噪声污染的小波系数,其中惩罚是适应每个分辨率尺度的,权重是适应每个尺度和每个位置的。所提出的PWLS方法基于以下观察结果:(1)对数变换后的低剂量CT噪声正弦图可建模为信号相关的高斯变量,且样本方差取决于样本均值;(2)当噪声恢复适应不同分辨率级别时,其效果会更显著。通过实验研究验证了所提出的多尺度PWLS方法的有效性。通过噪声分辨率权衡措施量化了多尺度方法相对于单尺度方法的增益。