Penalized-likelihood sinogram smoothing for low-dose CT
Penalized-likelihood sinogram smoothing for low-dose CT
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DOI：
10.1118/1.1915015
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发表时间：
2005-06-01
期刊：
影响因子：
3.8
通讯作者：
La Rivière, PJ
中科院分区：
文献类型：
--
作者：
La Rivière, PJ
我们已经开发了一种正弦图平滑方法，低剂量计算机断层扫描（CT），旨在估计重建所需的线积分的噪声测量，通过最大化惩罚似然目标函数。最大化是通过使用可分离的抛物面代理框架的算法。该方法克服了以前提出的基于样条函数的惩罚似然正弦图平滑方法的一些计算限制，并且发现它比这种基于样条函数的方法以及现有的自适应滤波方法产生更好的分辨率-方差权衡。当应用于CT筛查检查中采集的低剂量数据时，这种正弦图平滑方法可能是有价值的，例如那些被考虑用于肺结节检测的数据。(c)2005年美国医学物理学家协会。
We have developed a sinogram smoothing approach for low-dose computed tomography (CT) that seeks to estimate the line integrals needed for reconstruction from the noisy measurements by maximizing a penalized-likelihood objective function. The maximization is performed by an algorithm derived by use of the separable paraboloidal surrogates framework. The approach overcomes some of the computational limitations of a previously proposed spline-based penalized-likelihood sinogram smoothing approach, and it is found to yield better resolution-variance tradeoff's than this spline-based approach as well an existing adaptive filtering approach. Such sinogram smoothing approaches could be valuable when applied to the low-dose data acquired in CT screening exams, such as those being considered for lung-nodule detection. (c) 2005 American Association of Physicists in Medicine.