From the Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333, Burnet Ave, Cincinnati, OH 45329; and Department of Radiology, University of Cincinnati Medical School, Cincinnati, Ohio.
Radiology. 2021 Jan;298(1):180-188. doi: 10.1148/radiol.2020202317. Epub 2020 Nov 17.
Background CT deep learning reconstruction (DLR) algorithms have been developed to remove image noise. How the DLR affects image quality and radiation dose reduction has yet to be fully investigated. Purpose To investigate a DLR algorithm's dose reduction and image quality improvement for pediatric CT. Materials and Methods DLR was compared with filtered back projection (FBP), statistical-based iterative reconstruction (SBIR), and model-based iterative reconstruction (MBIR) in a retrospective study by using data from CT examinations of pediatric patients (February to December 2018). A comparison of object detectability for 15 objects (diameter, 0.5-10 mm) at four contrast difference levels (50, 150, 250, and 350 HU) was performed by using a non-prewhitening-matched mathematical observer model with eye filter (), task transfer function, and noise power spectrum analysis. Object detectability was assessed by using area under the curve analysis. Three pediatric radiologists performed an observer study to assess anatomic structures with low object-to-background signal and contrast to noise in the azygos vein, right hepatic vein, common bile duct, and superior mesenteric artery. Observers rated from 1 to 10 (worst to best) for edge definition, quantum noise level, and object conspicuity. Analysis of variance and Tukey honest significant difference post hoc tests were used to analyze differences between reconstruction algorithms. Results Images from 19 patients (mean age, 11 years ± 5 [standard deviation]; 10 female patients) were evaluated. Compared with FBP, SBIR, and MBIR, DLR demonstrated improved object detectability by 51% (16.5 of 10.9), 18% (16.5 of 13.9), and 11% (16.5 of 14.8), respectively. DLR reduced image noise without noise texture effects seen with MBIR. Radiologist ratings were 7 ± 1 (DLR), 6.2 ± 1 (MBIR), 6.2 ± 1 (SBIR), and 4.6 ± 1 (FBP); two-way analysis of variance showed a difference on the basis of reconstruction type ( < .001). Radiologists consistently preferred DLR images (intraclass correlation coefficient, 0.89; 95% CI: 0.83, 0.93). DLR demonstrated 52% (1 of 2.1) greater dose reduction than SBIR. Conclusion The DLR algorithm improved image quality and dose reduction without sacrificing noise texture and spatial resolution. © RSNA, 2020
背景 CT 深度学习重建（DLR）算法已被开发用于去除图像噪声。但是，DLR 如何影响图像质量和减少辐射剂量仍有待充分研究。目的 研究 DLR 算法在儿科 CT 中的剂量降低和图像质量改善效果。材料与方法 回顾性研究使用了 2018 年 2 月至 12 月期间儿科患者 CT 检查的数据，对 DLR 与滤波反投影（FBP）、基于统计的迭代重建（SBIR）和基于模型的迭代重建（MBIR）进行了比较。使用具有眼部滤波器（）、任务传递函数和噪声功率谱分析的非预白化匹配数学观察者模型，对 15 个直径为 0.5-10mm 的物体在 4 个对比差异水平（50、150、250 和 350HU）的物体可检测性进行了比较。使用曲线下面积分析评估物体可检测性。3 名儿科放射科医生进行了一项观察者研究，以评估在奇静脉、右肝静脉、胆总管和肠系膜上动脉中具有低物体与背景信号对比和噪声的解剖结构。观察者对边缘定义、量子噪声水平和物体显著性进行了 1 到 10 分（最差到最好）的评分。方差分析和 Tukey 诚实显著差异事后检验用于分析重建算法之间的差异。结果 19 名患者（平均年龄，11 岁±5[标准差]；10 名女性）的图像得到了评估。与 FBP、SBIR 和 MBIR 相比，DLR 分别提高了 51%（10.9 的 16.5）、18%（13.9 的 16.5）和 11%（14.8 的 16.5）的物体可检测性。DLR 降低了图像噪声，而 MBIR 则没有出现噪声纹理效应。放射科医生的评分分别为 7±1（DLR）、6.2±1（MBIR）、6.2±1（SBIR）和 4.6±1（FBP）；双向方差分析显示基于重建类型的差异（<.001）。放射科医生始终更喜欢 DLR 图像（组内相关系数，0.89；95%置信区间：0.83，0.93）。与 SBIR 相比，DLR 显示出 52%（2.1 的 1）更大的剂量降低。结论 DLR 算法改善了图像质量和剂量降低，同时没有牺牲噪声纹理和空间分辨率。
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