基于投影域多尺度分解和惩罚加权最小二乘法的 CT 统计降噪。
Statistical CT noise reduction with multiscale decomposition and penalized weighted least squares in the projection domain.
机构信息
Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA.
出版信息
Med Phys. 2012 Sep;39(9):5498-512. doi: 10.1118/1.4745564.
PURPOSES
The suppression of noise in x-ray computed tomography (CT) imaging is of clinical relevance for diagnostic image quality and the potential for radiation dose saving. Toward this purpose, statistical noise reduction methods in either the image or projection domain have been proposed, which employ a multiscale decomposition to enhance the performance of noise suppression while maintaining image sharpness. Recognizing the advantages of noise suppression in the projection domain, the authors propose a projection domain multiscale penalized weighted least squares (PWLS) method, in which the angular sampling rate is explicitly taken into consideration to account for the possible variation of interview sampling rate in advanced clinical or preclinical applications.
METHODS
The projection domain multiscale PWLS method is derived by converting an isotropic diffusion partial differential equation in the image domain into the projection domain, wherein a multiscale decomposition is carried out. With adoption of the Markov random field or soft thresholding objective function, the projection domain multiscale PWLS method deals with noise at each scale. To compensate for the degradation in image sharpness caused by the projection domain multiscale PWLS method, an edge enhancement is carried out following the noise reduction. The performance of the proposed method is experimentally evaluated and verified using the projection data simulated by computer and acquired by a CT scanner.
RESULTS
The preliminary results show that the proposed projection domain multiscale PWLS method outperforms the projection domain single-scale PWLS method and the image domain multiscale anisotropic diffusion method in noise reduction. In addition, the proposed method can preserve image sharpness very well while the occurrence of "salt-and-pepper" noise and mosaic artifacts can be avoided.
CONCLUSIONS
Since the interview sampling rate is taken into account in the projection domain multiscale decomposition, the proposed method is anticipated to be useful in advanced clinical and preclinical applications where the interview sampling rate varies.
目的
在 X 射线计算机断层扫描（CT）成像中抑制噪声对于诊断图像质量和潜在的辐射剂量节省具有临床意义。为此，已经提出了在图像域或投影域中使用统计降噪方法的方法，这些方法采用多尺度分解来提高噪声抑制的性能，同时保持图像锐度。认识到投影域中噪声抑制的优势，作者提出了一种投影域多尺度惩罚加权最小二乘（PWLS）方法，其中明确考虑了角采样率，以考虑在高级临床或临床前应用中可能变化的访谈采样率。
方法
通过将图像域中的各向同性扩散偏微分方程转换为投影域，推导出投影域多尺度 PWLS 方法，其中进行了多尺度分解。采用马尔可夫随机场或软阈值目标函数，投影域多尺度 PWLS 方法处理每个尺度的噪声。为了补偿投影域多尺度 PWLS 方法引起的图像锐度下降，在降噪后进行边缘增强。使用计算机模拟和 CT 扫描仪获取的投影数据对所提出方法的性能进行了实验评估和验证。
结果
初步结果表明，与投影域单尺度 PWLS 方法和图像域多尺度各向异性扩散方法相比，所提出的投影域多尺度 PWLS 方法在噪声抑制方面表现更好。此外，该方法可以很好地保持图像锐度，同时避免出现“椒盐”噪声和镶嵌伪影。
结论
由于在投影域多尺度分解中考虑了访谈采样率，因此预计所提出的方法在访谈采样率变化的高级临床和临床前应用中很有用。
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