Sidra Medical and Research Center, Department of Diagnostic Imaging, Doha, Qatar
Yale University School of Medicine, Department of Diagnostic Radiology, New Haven, Connecticut.
J Nucl Med. 2014 Dec;55(12):1998-2002. doi: 10.2967/jnumed.114.140129. Epub 2014 Nov 13.
This article explores how one can lower the injected (18)F-FDG dose while maintaining validity in comparing standardized uptake values (SUVs) between studies. Variations of the SUV within each lesion were examined at different acquisition times.
Our protocol was approved by either the Human Investigation Committee or the Institutional Review Board. All 120 PET datasets were acquired continuously for 180 s per bed position in list mode and were reconstructed to obtain 30-, 60-, 90-, 120-, 150-, and 180-s-per-bed-position PET images with registration to a single set of nondiagnostic CT images. Qualitative assessment of the images was performed separately for correlation. The SUV measurements of each lesion were computed and normalized to the 180-s acquisition values to create a stabilization factor. These stabilization factors were used to demonstrate a predictable trend of stabilization over time. The variances of the stabilization factors over the entire dataset, composed of several tumor types over a range of sizes, were compared for each time point with the corresponding 150-s time point using a 2-sided F test, which has similar values to the 180-s time point.
The variance of the data decreased with increasing acquisition time and with increasing dose but leveled off for sufficiently long acquisitions.
Through the statistical analysis of SUVs for increasing acquisition times and visual evaluation of the plots, we developed and hereby propose an algorithm that can be used to seek the maximum reduction in administered (18)F-FDG dose while preserving the validity of SUV comparisons.
本文探讨了如何在保持研究间标准化摄取值（SUV）可比性的同时降低注射（18）F-FDG 的剂量。在不同的采集时间，检查了每个病变内 SUV 的变化。
我们的方案获得了人类调查委员会或机构审查委员会的批准。所有 120 个 PET 数据集以列表模式连续采集，每个床位位置采集 180 秒，重建以获得 30、60、90、120、150 和 180 秒/床位位置的 PET 图像，并与一组非诊断性 CT 图像进行配准。图像的定性评估是分开进行的，以便进行相关性分析。对每个病变的 SUV 测量值进行计算，并归一化为 180 秒采集值，以创建稳定化因子。这些稳定化因子用于展示随时间的稳定化的可预测趋势。使用双侧 F 检验比较整个数据集的方差，该数据集由多种肿瘤类型组成，大小范围较广，与每个时间点的相应 150 秒时间点进行比较，F 检验具有与 180 秒时间点相似的值。
随着采集时间的增加和剂量的增加，数据的方差减小，但对于足够长的采集时间，方差趋于稳定。
通过对 SUV 进行越来越长时间的采集的统计分析和对图的直观评估，我们开发了一种算法，并在此提出了一种算法，该算法可用于在保持 SUV 比较有效性的同时，最大限度地减少给药（18）F-FDG 的剂量。
