1 Keck Hospital, University of Southern California, 1500 San Pablo St, 2nd Fl Radiology, Los Angeles, CA 90033.
2 Department of Radiology, University of California, Los Angeles, Los Angeles, CA.
AJR Am J Roentgenol. 2018 Sep;211(3):548-556. doi: 10.2214/AJR.17.19245. Epub 2018 Jul 24.
The objective of this study is to compare forward-projected model-based iterative reconstruction solution (FIRST), a newer fully iterative CT reconstruction method, with adaptive iterative dose reduction 3D (AIDR 3D) in low-dose screening CT for lung cancer. Differences in image noise, image quality, and pulmonary nodule detection, size, and characterization were specifically evaluated.
Low-dose chest CT images obtained for 50 consecutive patients between December 2015 and January 2016 were retrospectively reviewed. Images were reconstructed using FIRST and AIDR 3D for both lung and soft-tissue reconstruction. Images were independently reviewed to assess image noise, subjective image quality (with use of a 5-point Likert scale, with 1 denoting far superior image quality; 2, superior quality; 3, equivalent quality; 4, inferior quality; and 5, far inferior quality), pulmonary nodule count, size of the largest pulmonary nodule, and characterization of the largest pulmonary nodule (i.e., solid, part solid, or ground glass).
Across all 50 cases, measured image noise was lower with FIRST than with AIDR 3D (lung window, 44% reduction, 41 ± 7 vs 74 ± 8 HU, respectively; soft-tissue window, 32% reduction, 11 ± 2 vs 16 ± 2 HU, respectively). Readers subjectively rated images obtained with FIRST as comparable to images obtained with AIDR 3D (mean [± SD] Likert score for FIRST vs AIDR 3D, 3.2 ± 0.3 for soft-tissue reconstructions and 3.0 ± 0.3 for lung reconstructions). For each reader, very good agreement regarding nodule count was noted between FIRST and AIDR 3D (interclass correlation coefficient [ICC], 0.83 for reader 1 and 0.78 for reader 2). Excellent agreement regarding nodule size (ICC, 0.99 for reader 1 and 0.99 for reader 2) and characterization of the largest nodule (kappa value, 0.92 for reader 1 and 0.82 for reader 2) also existed.
Images reconstructed with FIRST are superior to those reconstructed AIDR 3D with regard to image noise and are equivalent with regard to subjective image quality, pulmonary nodule count, and nodule characterization.
本研究旨在比较正向投影模型迭代重建（FIRST）和自适应迭代剂量降低三维重建（AIDR 3D）两种全新的完全迭代 CT 重建方法，以评估其在肺癌低剂量筛查 CT 中的应用效果。特别评估了图像噪声、图像质量以及肺结节检测、大小和特征。
回顾性分析了 2015 年 12 月至 2016 年 1 月期间连续 50 例患者的低剂量胸部 CT 图像。使用 FIRST 和 AIDR 3D 对肺和软组织进行重建。对图像进行独立评估，以评估图像噪声、主观图像质量（使用 5 分李克特量表，1 表示图像质量远优于 2 表示质量优于 3 表示质量相等 4 表示质量差 5 表示图像质量差）、肺结节计数、最大肺结节的大小以及最大肺结节的特征（实性、部分实性或磨玻璃）。
在所有 50 例患者中，FIRST 组的图像噪声均低于 AIDR 3D 组（肺窗，44%降低，分别为 41 ± 7 HU 和 74 ± 8 HU；软组织窗，32%降低，分别为 11 ± 2 HU 和 16 ± 2 HU）。读者主观评价 FIRST 组的图像与 AIDR 3D 组的图像质量相当（FIRST 组和 AIDR 3D 组的平均（±标准差）李克特评分，软组织重建分别为 3.2 ± 0.3，肺重建分别为 3.0 ± 0.3）。每位读者的结节计数在 FIRST 组和 AIDR 3D 组之间具有很好的一致性（读者 1 的组内相关系数为 0.83，读者 2 的组内相关系数为 0.78）。结节大小（读者 1 的组内相关系数为 0.99，读者 2 的组内相关系数为 0.99）和最大结节特征（读者 1 的一致性kappa 值为 0.92，读者 2 的一致性kappa 值为 0.82）也存在极好的一致性。
FIRST 组的图像在噪声方面优于 AIDR 3D 组，在主观图像质量、肺结节计数和结节特征方面与 AIDR 3D 组相当。
