Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan.
AJR Am J Roentgenol. 2012 Sep;199(3):595-601. doi: 10.2214/AJR.11.8174.
The purpose of this study is to investigate the effect of a novel reconstruction algorithm, adaptive iterative dose reduction using 3D processing, on emphysema quantification by low-dose CT.
Twenty-six patients who had undergone standard-dose (150 mAs) and low-dose (25 mAs) CT scans were included in this retrospective study. Emphysema was quantified by several quantitative measures, including emphysema index given by the percentage of lung region with low attenuation (lower than -950 HU), the 15th percentile of lung density, and size distribution of low-attenuation lung regions, on standard-dose CT images reconstructed without adaptive iterative dose reduction using 3D processing and on low-dose CT images reconstructed both without and with adaptive iterative dose reduction using 3D processing. The Bland-Altman analysis was used to assess whether the agreement between emphysema quantifications on low-dose CT and on standard-dose CT was improved by the use of adaptive iterative dose reduction using 3D processing.
For the emphysema index, the mean differences between measurements on low-dose CT and on standard-dose CT were 1.98% without and -0.946% with the use of adaptive iterative dose reduction using 3D processing. For 15th percentile of lung density, the mean differences without and with adaptive iterative dose reduction using 3D processing were -6.67 and 1.28 HU, respectively. For the size distribution of low-attenuation lung regions, the ranges of the mean relative differences without and with adaptive iterative dose reduction using 3D processing were 21.4-85.5% and -14.1% to 11.2%, respectively. For 15th percentile of lung density and the size distribution of low-attenuation lung regions, the agreement was thus improved by the use of adaptive iterative dose reduction using 3D processing.
The use of adaptive iterative dose reduction using 3D processing resulted in greater consistency of emphysema quantification by low-dose CT, with quantification by standard-dose CT.
本研究旨在探讨一种新的重建算法，即基于三维处理的自适应迭代剂量降低，对低剂量 CT 肺气肿定量的影响。
本回顾性研究纳入了 26 例接受标准剂量（150 mAs）和低剂量（25 mAs）CT 扫描的患者。使用标准剂量 CT 图像（未使用基于三维处理的自适应迭代剂量降低）和低剂量 CT 图像（既未使用也使用了基于三维处理的自适应迭代剂量降低），通过多种定量指标，包括低衰减区域（低于-950 HU）百分比的肺气肿指数、肺密度的 15 百分位数以及低衰减肺区域的大小分布，对肺气肿进行定量。采用 Bland-Altman 分析评估使用基于三维处理的自适应迭代剂量降低是否改善了低剂量 CT 与标准剂量 CT 之间肺气肿定量的一致性。
对于肺气肿指数，低剂量 CT 与标准剂量 CT 测量值之间的平均差异在未使用自适应迭代剂量降低时为 1.98%，在使用时为-0.946%。对于肺密度的 15 百分位数，未使用和使用基于三维处理的自适应迭代剂量降低时的平均差异分别为-6.67 HU 和 1.28 HU。对于低衰减肺区域的大小分布，未使用和使用基于三维处理的自适应迭代剂量降低时的平均相对差异范围分别为 21.4%-85.5%和-14.1%至 11.2%。对于肺密度的 15 百分位数和低衰减肺区域的大小分布，使用基于三维处理的自适应迭代剂量降低改善了一致性。
使用基于三维处理的自适应迭代剂量降低可以提高低剂量 CT 肺气肿定量的一致性，与标准剂量 CT 定量结果更一致。
