Postreconstruction filtering of 3D PET images by using weighted higher-order singular value decomposition.
Postreconstruction filtering of 3D PET images by using weighted higher-order singular value decomposition.
复制标题
使用加权高阶奇异值分解对 3D PET 图像进行重建后滤波
DOI：
10.1186/s12938-016-0221-y
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发表时间：
2016-08-27
影响因子：
3.9
通讯作者：
Tian J
中科院分区：
文献类型：
--
作者：
Liu H;Wang K;Tian J
背景正电子发射断层扫描（PET）由于受注入剂量和采集时间的限制，一直存在着较高的噪声，尤其是在动态PET成像研究中。为了提高PET图像的质量，已经引入了几种方法来抑制噪声。然而，传统的滤波器经常模糊图像边缘，或擦除小细节，或依赖于多个参数。为了解决这样的问题，非局部去噪方法已被适配到去噪PET images.MethodsIn本文中，我们提出了使用加权高阶奇异值分解PET图像去噪。我们首先将PET图像中的噪声建模为泊松分布。然后，我们通过使用anscombe根变换将噪声转换为加性高斯噪声。最后，我们使用提出的高阶奇异值分解（HOSVD）为基础的算法去噪变换后的图像。去噪的结果进行了比较，从一些一般的过滤器通过执行物理幻影和mice studies.ResultsCompared其他常用的过滤器，HOSVD为基础的去噪算法可以更好地保持边界和定量精度的结果。基于HOSVD的方法还可以保留PET图像的空间分辨率和低活性特征。与标准HOSVD算法相比，加权HOSVD算法能有效抑制阶梯状伪影，且时间消耗约为Wiener增强HOSVD算法的一半。结论加权HOSVD算法能在抑制噪声的同时更好地保持PET图像的边界和数量。
BackgroundPositron emission tomography (PET) always suffers from high levels of noise due to the constraints of the injected dose and acquisition time, especially in the studies of dynamic PET imaging. To improve the quality of PET image, several approaches have been introduced to suppress noise. However, traditional filters often blur the image edges, or erase small detail, or rely on multiple parameters. In order to solve such problems, nonlocal denoising methods have been adapted to denoise PET images.MethodsIn this paper, we propose to use the weighted higher-order singular value decomposition for PET image denoising. We first modeled the noise in the PET image as Poisson distribution. Then, we transformed the noise to an additive Gaussian noise by use of the anscombe root transformation. Finally, we denoised the transformed image using the proposed higher-order singular value decomposition (HOSVD)-based algorithms. The denoised results were compared with results from some general filters by performing physical phantom and mice studies.ResultsCompared to other commonly used filters, HOSVD-based denoising algorithms can preserve boundaries and quantitative accuracy better. The spatial resolution and the low activity features in PET image also can be preserved by use of HOSVD-based methods. Comparing with the standard HOSVD-based algorithm, the proposed weighted HOSVD algorithm can suppress the stair-step artifact, and the time-consumption is about half of that needed by the Wiener-augmented HOSVD algorithm.ConclusionsThe proposed weighted HOSVD denoising algorithm can suppress noise while better preserving of boundary and quantity in PET images.
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影响因子：
3.5
作者：
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通讯作者：
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影响因子：
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DOI：
10.1109/tpami.2012.140
发表时间：
2013-04-01
影响因子：
23.6
作者：
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通讯作者：
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