Tong Shan, Alessio Adam M, Kinahan Paul E
Department of Radiology, University of Washington, Seattle, WA 98195 USA.
IEEE Nucl Sci Symp Conf Rec (1997). 2009 Oct 24;2009(2009):3042-3047. doi: 10.1109/nssmic.2009.5401574.
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.
在PET图像重建中加入精确的系统建模会得到具有独特噪声纹理和特征的图像。特别是，将点扩散函数（PSF）纳入系统模型已被证明在视觉上可以降低图像噪声，但噪声特性尚未得到充分研究。这项工作对不同重建方法和参数组合下的噪声和信号特性进行了系统评估。我们评估了两种全三维PET重建算法：（1）对扫描仪响应线进行精确建模的有序子集最大期望值算法（OSEM+LOR），（2）结合响应线和测量得到的点扩散函数的有序子集最大期望值算法（OSEM+LOR+PSF），并结合4个后滤波参数和1至10次迭代的效果进行评估。我们使用了一个经过修改的NEMA IQ体模，其填充有68Ge，由6个不同大小的热球体组成，目标/背景比为4:1。该体模在临床系统上以三维模式扫描50次，以提供独立的噪声实现。使用不同的重建参数，用OSEM+LOR和OSEM+LOR+PSF对数据进行重建。由于可以获得多个实现，采用4个指标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变化的度量，而NEMA噪声和总体噪声则量化区域间变化。除了4个噪声指标外，我们还使用公认的信号强度测量方法（恢复系数、定量信噪比）评估信噪比性能，并研究不同指标之间 的关系。从分析结果来看，对于所有不同的重建方法和参数组合，观察到NEMA噪声和总体噪声之间存在线性相关性，这表明在实际中无法获得多次扫描实现时，NEMA风格的噪声是总体噪声的合理替代。在相同的迭代次数下，添加PSF可使未滤波图像的粗糙度降低约35%，而添加PSF并不会降低NEMA风格噪声或总体噪声。当跨实现测量噪声时，基于PSF的方法在一系列重建参数下提供了略有改善（7%）的信噪比性能。
