Department of Radiology, University of Washington, Seattle, WA 98195, USA.
Phys Med Biol. 2010 Mar 7;55(5):1453-73. doi: 10.1088/0031-9155/55/5/013. Epub 2010 Feb 11.
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 four post-reconstruction filtering parameters and 1-10 iterations, representing a range of clinically acceptable settings. We used a modified NEMA image quality (IQ) phantom, which was filled with 68Ge and consisted of six 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, and our implementations of the algorithms match the vendor's product algorithms. With access to multiple realizations, background noise characteristics were quantified with four metrics. Image roughness and the standard deviation image measured the pixel-to-pixel variation; background variability and ensemble noise quantified the region-to-region variation. Image roughness is the image noise perceived when viewing an individual image. At matched iterations, the addition of PSF leads to images with less noise defined as image roughness (reduced by 35% for unfiltered data) and as the standard deviation image, while it has no effect on background variability or ensemble noise. In terms of signal to noise performance, PSF-based reconstruction has a 7% improvement in contrast recovery at matched ensemble noise levels and 20% improvement of quantitation SNR in unfiltered data. In addition, the relations between different metrics are studied. A linear correlation is observed between background variability and ensemble noise for all different combinations of reconstruction methods and parameters, suggesting that background variability is a reasonable surrogate for ensemble noise when multiple realizations of scans are not available.
在 PET 图像重建中加入准确的系统建模会导致图像具有明显不同的噪声纹理和特征。特别是，将点扩散函数（PSF）纳入系统模型中已被证明可以在视觉上降低图像噪声，但噪声特性尚未得到彻底研究。本工作系统地评估了不同重建方法和参数组合中的噪声和信号特性。我们评估了两种完全 3D PET 重建算法：（1）使用精确扫描仪线响应模型的 OSEM（OSEM+LOR），（2）使用线响应和测量 PSF 的 OSEM（OSEM+LOR+PSF），结合四种后重建滤波参数和 1-10 次迭代的影响，代表了一系列可接受的临床设置。我们使用了经过修改的 NEMA 图像质量（IQ）体模，该体模用 68Ge 填充，由六个不同大小的热球组成，目标/背景比为 4:1。该体模在临床系统上以 3D 模式扫描了 50 次，以提供独立的噪声实现。使用 OSEM+LOR 和 OSEM+LOR+PSF 对数据进行重建，并使用不同的重建参数对算法进行了我们的实现，与供应商的产品算法相匹配。通过访问多个实现，可以使用四个指标来量化背景噪声特性。图像粗糙度和标准偏差图像测量了像素到像素的变化；背景变化和整体噪声量化了区域到区域的变化。图像粗糙度是在查看单个图像时感知到的图像噪声。在匹配的迭代次数下，添加 PSF 会导致图像噪声降低，定义为图像粗糙度（未过滤数据减少 35%）和标准偏差图像，而对背景变化或整体噪声没有影响。在信噪比性能方面，在匹配的整体噪声水平下，基于 PSF 的重建对比度恢复提高了 7%，在未过滤数据中定量 SNR 提高了 20%。此外，还研究了不同指标之间的关系。对于所有不同的重建方法和参数组合，观察到背景变化和整体噪声之间存在线性相关性，这表明在无法获得扫描的多个实现时，背景变化是整体噪声的合理替代。
