Clinical Imaging Physics Group, Department of Radiology, Duke University Medical Center, Durham, North Carolina.
J Nucl Med. 2014 Jan;55(1):169-74. doi: 10.2967/jnumed.113.125450. Epub 2013 Nov 8.
Because γ cameras are generally susceptible to environmental conditions and system vulnerabilities, they require routine evaluation of uniformity performance. The metrics for such evaluations are commonly pixel value-based. Although these metrics are typically successful at identifying regional nonuniformities, they often do not adequately reflect subtle periodic structures; therefore, additional visual inspections are required. The goal of this project was to develop, test, and validate a new uniformity analysis metric capable of accurately identifying structures and patterns present in nuclear medicine flood-field uniformity images.
A new uniformity assessment metric, termed the structured noise index (SNI), was based on the 2-dimensional noise power spectrum (NPS). The contribution of quantum noise was subtracted from the NPS of a flood-field uniformity image, resulting in an NPS representing image artifacts. A visual response filter function was then applied to both the original NPS and the artifact NPS. A single quantitative score was calculated on the basis of the magnitude of the artifact. To verify the validity of the SNI, an observer study was performed with 5 expert nuclear medicine physicists. The correlation between the SNI and the visual score was assessed with Spearman rank correlation analysis. The SNI was also compared with pixel value-based assessment metrics modeled on the National Electrical Manufacturers Association standard for integral uniformity in both the useful field of view (UFOV) and the central field of view (CFOV).
The SNI outperformed the pixel value-based metrics in terms of its correlation with the visual score (ρ values for the SNI, integral UFOV, and integral CFOV were 0.86, 0.59, and 0.58, respectively). The SNI had 100% sensitivity for identifying both structured and nonstructured nonuniformities; for the integral UFOV and CFOV metrics, the sensitivities were only 62% and 54%, respectively. The overall positive predictive value of the SNI was 87%; for the integral UFOV and CFOV metrics, the positive predictive values were only 67% and 50%, respectively.
The SNI accurately identified both structured and nonstructured flood-field nonuniformities and correlated closely with expert visual assessment. Compared with traditional pixel value-based analysis, the SNI showed superior performance in terms of its correlation with visual perception. The SNI method is effective for detecting and quantifying visually apparent nonuniformities and may reduce the need for more subjective visual analyses.
由于 γ 相机通常容易受到环境条件和系统漏洞的影响，因此需要对其均匀性性能进行常规评估。此类评估的指标通常是基于像素值的。尽管这些指标通常能够成功识别区域非均匀性，但它们往往不能充分反映细微的周期性结构；因此，需要进行额外的目视检查。本项目的目标是开发、测试和验证一种新的均匀性分析指标，该指标能够准确识别核医学填充场均匀性图像中存在的结构和模式。
一种新的均匀性评估指标，称为结构噪声指数 (SNI)，基于二维噪声功率谱 (NPS)。从填充场均匀性图像的 NPS 中减去量子噪声的贡献，得到代表图像伪影的 NPS。然后，对原始 NPS 和伪影 NPS 应用视觉响应滤波器函数。根据伪影的幅度计算出一个单一的定量分数。为了验证 SNI 的有效性，进行了一项由 5 位核医学物理学家组成的观察者研究。使用 Spearman 秩相关分析评估 SNI 与视觉评分之间的相关性。还将 SNI 与基于 National Electrical Manufacturers Association 标准的像素值为基础的评估指标进行了比较，该标准用于测量有用视野 (UFOV) 和中央视野 (CFOV) 内的积分均匀性。
SNI 在与视觉评分的相关性方面优于基于像素值的指标（SNI、积分 UFOV 和积分 CFOV 的ρ值分别为 0.86、0.59 和 0.58）。SNI 对识别结构化和非结构化非均匀性具有 100%的灵敏度；对于积分 UFOV 和 CFOV 指标，灵敏度分别为 62%和 54%。SNI 的总体阳性预测值为 87%；对于积分 UFOV 和 CFOV 指标，阳性预测值分别为 67%和 50%。
SNI 能够准确识别结构化和非结构化的填充场非均匀性，与专家的视觉评估密切相关。与传统的基于像素值的分析相比，SNI 在与视觉感知的相关性方面表现出更好的性能。SNI 方法可有效检测和量化明显的非均匀性，并可能减少对更主观的视觉分析的需求。
