Evaluation of Noise Properties in PSF-Based PET Image Reconstruction.
Evaluation of Noise Properties in PSF-Based PET Image Reconstruction.
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基于 PSF 的 PET 图像重建中的噪声特性评估。
DOI：
10.1109/nssmic.2009.5401574
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Kinahan,PaulE
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文献类型：
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作者：
Tong,Shan;Alessio,AdamM;Kinahan,PaulE
在PET图像重建中加入了精确的系统建模，使得图像具有明显的噪声、纹理和特征。特别是，将点扩展函数(PSF)结合到系统模型中已被证明在视觉上降低了图像噪声，但其噪声特性尚未被彻底研究。这项工作提供了在不同的重建方法和参数组合下的噪声和信号特性的系统评估。我们评估了两种全三维PET重建算法：(1)精确模拟响应线的OSEM(OSM+LOR)，(2)结合4个后滤波参数和1-10次迭代的结合响应线和测量点扩散函数的OSEM(OSEM+LOR+PSF)。我们使用了一个改良的NEMA IQ体模，该体模填充了68Ge，由6个不同大小的热球组成，靶/背景比为4：1。在临床系统上对该体模进行了50次3D扫描，以提供独立的噪声实现。采用不同的重建参数，分别采用OSM+LOR和OSEM+LOR+PSF进行重建。通过多个实现，采用4个度量来量化重建图像中的噪声特征。图像粗糙度和标准差图像是像素间差异的量度，而NEMA和集成噪声则量化区域间的差异。除了4个噪声指标外，我们还使用公认的信号强度指标(恢复系数、量化信噪比)来评估信噪比性能，并研究了不同指标之间的关系。从分析结果可以看出，对于所有不同的重建方法和参数组合，NEMA噪声和集合噪声之间存在线性相关，这表明当实际中不能实现多种扫描时，NEMA风格的噪声是集合噪声的合理替代。在相同的迭代次数下，PSF的加入使未过滤图像的图像粗糙度降低了大约35%，而PSF的加入并没有减少NEMA风格或整体噪声。当跨实现测量噪声时，基于PSF的方法在一系列重建参数上提供略微改善(7%)的信噪比性能。
The addition of accurate system modeling in PET image reconstruction results in images with distinct noise texture and characteristics. In particular, the incorporation of point spread functions (PSF) into the system model has been shown to visually reduce image noise, but the noise properties have not been thoroughly studied. This work offers a systematic evaluation of noise and signal properties in different combinations of reconstruction methods and parameters. We evaluate two fully-3D PET reconstruction algorithms: (1) OSEM with exact scanner line of response modeled (OSEM+LOR), (2) OSEM with line of response and a measured point spread function incorporated (OSEM+LOR+PSF), in combination with the effects of 4 post filtering parameters and 1-10 iterations. We used a modified NEMA IQ phantom, which was filled with 68Ge and consisted of 6 hot spheres of different sizes with a target/background ratio of 4:1. The phantom was scanned 50 times in 3D mode on a clinical system to provide independent noise realizations. Data were reconstructed with OSEM+LOR and OSEM+LOR+PSF using different reconstruction parameters. With access to multiple realizations, 4 metrics are adopted to quantify the noise characteristics in the reconstructed images. Image roughness and the standard deviation image are measures of the pixel-to-pixel variation, while NEMA and ensemble noises quantify the region-to-region variation. In addition to 4 noise metrics, we also evaluate signal to noise performance with accepted signal strength measures (recovery coefficient, SNR for quantitation), and study the relations between different metrics. From the analysis results, a linear correlation is observed between NEMA noise and ensemble noise for all different combinations of reconstruction methods and parameters, suggesting that NEMA style noise is a reasonable surrogate for ensemble noise when multiple realizations of scans are not available in practice. At the same number of iterations, the addition of PSF reduces image roughness for unfiltered images by roughly 35%, while the addition of PSF does not reduce NEMA style or ensemble noise. When noise is measured across realizations, the PSF based method offers slightly improved (7%) signal to noise performance across a range of reconstruction parameters.