A theoretically based pre-reconstructing filter for spatio-temporal noise reduction in gated cardiac SPECT
A theoretically based pre-reconstructing filter for spatio-temporal noise reduction in gated cardiac SPECT
复制标题
一种基于理论的预重构滤波器，用于门控心脏 SPECT 中的时空降噪
DOI：
10.1109/nssmic.2000.950083
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发表时间：
2000
期刊：
影响因子：
--
通讯作者：
Zhengrong Liang
中科院分区：
文献类型：
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作者：
Hongbing Lu;G. Han;Dongqing Chen;Lihong C. Li;Zhengrong Liang
在动态SPECT研究中，除了每个时间帧内的空间相关性（即，3D正弦图）。我们提出了一种基于理论的多帧滤波算法，该算法同时考虑了空间和时间相关性，用于恢复由泊松噪声退化的门控正弦图。通过首先将Anscombe变换应用于所有正弦图数据，极大地简化了该空间-时间滤波任务，该Anscombe变换将泊松分布噪声转换为具有恒定方差的高斯分布噪声（即，噪声与信号无关）。通过对Anscombe变换后的数据序列进行时间上的Karhunen-Loeve（K-L）变换，进一步将滤波任务从4D问题简化为3D空间过程。在K-L域中，对于所有分量，保持恒定方差的噪声特性，而对于较低特征值分量，信噪比单调减小。一个准确的维纳滤波器，然后构建一个3D数据集的每个组件。通过这种方法，时空滤波可以实现在一个合理的计算成本。计算机模拟是非常令人鼓舞的，通过视觉判断，相比逐帧的3D维纳滤波沿时间序列。
In dynamic SPECT studies, the acquired sinograms have both spatial and temporal correlations among the time sequence, in addition to the spatial correlation within each time frame (i.e., a 3D sinogram). We propose a theoretically based multi-frame filtering algorithm, which considers both the spatial and temporal correlations, for restoring the gated sinograms degraded by the Poisson noise. This spatio-temporal filtering task is greatly simplified by first applying the Anscombe transformation to all the sinogram data, which converts Poisson distributed noise into Gaussian distributed one with constant variance (i.e., the noise is signal independent). By performing temporal Karhunen-Loeve (K-L) transformation on the Anscombe transformed data sequence, the filtering task is further simplified from a 4D problem to a 3D spatial process. In the K-L domain, the noise property of constant variance remains for all components, while the signal-to-noise ratio decreases monotonically for lower eigenvalue components. An accurate Wiener filter is then constructed for each component of a 3D data set. By this approach, the spatiotemporal filtering can be achieved at a reasonable computational cost. The computer simulations are very encouraging, by visual judgement, as compared to frame-by-frame 3D Wiener filtering along the time sequence.