前瞻性评估深度学习图像重建在 Lung-RADS 和超低剂量胸部 CT 自动结节容积测量中的应用。
Prospective evaluation of deep learning image reconstruction for Lung-RADS and automatic nodule volumetry on ultralow-dose chest CT.
机构信息
Department of Radiology, Hanyang University Medical Center, Hanyang University College of Medicine, Seoul, Republic of Korea.
Department of Internal Medicine, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Korea.
出版信息
PLoS One. 2024 Feb 22;19(2):e0297390. doi: 10.1371/journal.pone.0297390. eCollection 2024.
PURPOSE
To prospectively evaluate whether Lung-RADS classification and volumetric nodule assessment were feasible with ultralow-dose (ULD) chest CT scans with deep learning image reconstruction (DLIR).
METHODS
The institutional review board approved this prospective study. This study included 40 patients (mean age, 66±12 years; 21 women). Participants sequentially underwent LDCT and ULDCT (CTDIvol, 0.96±0.15 mGy and 0.12±0.01 mGy) scans reconstructed with the adaptive statistical iterative reconstruction-V 50% (ASIR-V50) and DLIR. CT image quality was compared subjectively and objectively. The pulmonary nodules were assessed visually by two readers using the Lung-RADS 1.1 and automatically using a computerized assisted tool.
RESULTS
DLIR provided a significantly higher signal-to-noise ratio for LDCT and ULDCT images than ASIR-V50 (all P < .001). In general, DLIR showed superior subjective image quality for ULDCT images (P < .001) and comparable quality for LDCT images compared to ASIR-V50 (P = .01-1). The per-nodule sensitivities of observers for Lung-RADS category 3-4 nodules were 70.6-88.2% and 64.7-82.4% for DLIR-LDCT and DLIR-ULDCT images (P = 1) and categories were mostly concordant within observers. The per-nodule sensitivities of the computer-assisted detection for nodules ≥4 mm were 72.1% and 67.4% on DLIR-LDCT and ULDCT images (P = .50). The 95% limits of agreement for nodule volume differences between DLIR-LDCT and ULDCT images (-85.6 to 78.7 mm3) was similar to the within-scan nodule volume differences between DLIR- and ASIR-V50-LDCT images (-63.9 to 78.5 mm3), with volume differences smaller than 25% in 88.5% and 92.3% of nodules, respectively (P = .65).
CONCLUSION
DLIR enabled comparable Lung-RADS and volumetric nodule assessments on ULDCT images to LDCT images.
目的
前瞻性评估深度学习图像重建（DLIR）超低剂量（ULD）胸部 CT 扫描能否进行 Lung-RADS 分类和容积结节评估。
方法
本机构审查委员会批准了这项前瞻性研究。这项研究共纳入 40 名患者（平均年龄 66±12 岁；21 名女性）。参与者依次进行低剂量 CT（LDCT）和 ULDCT（CTDIvol 分别为 0.96±0.15 mGy 和 0.12±0.01 mGy）扫描，采用自适应统计迭代重建-V50%（ASIR-V50）和 DLIR 进行重建。对 CT 图像质量进行主观和客观比较。两名读者使用 Lung-RADS 1.1 对肺结节进行视觉评估，并使用计算机辅助工具进行自动评估。
结果
与 ASIR-V50 相比，DLIR 显著提高了 LDCT 和 ULDCT 图像的信噪比（均 P<0.001）。一般来说，DLIR 对 ULDCT 图像的主观图像质量优于 ASIR-V50（均 P<0.001），与 ASIR-V50 相比，LDCT 图像的质量相当（P=0.01-1）。观察者对 Lung-RADS 3-4 类结节的每例结节敏感度分别为 70.6-88.2%和 64.7-82.4%，用于 DLIR-LDCT 和 DLIR-ULDCT 图像（P=1），观察者内结节分类大多一致。计算机辅助检测对≥4mm 结节的每例结节敏感度分别为 72.1%和 67.4%，用于 DLIR-LDCT 和 ULDCT 图像（P=0.50）。DLIR-LDCT 和 ULDCT 图像之间的结节体积差异的 95%一致性界限（-85.6 至 78.7mm3）与 DLIR-和 ASIR-V50-LDCT 图像之间的扫描内结节体积差异（-63.9 至 78.5mm3）相似，体积差异小于 25%的结节分别占 88.5%和 92.3%（P=0.65）。
结论
DLIR 使 ULDCT 图像能够与 LDCT 图像进行可比的 Lung-RADS 和容积结节评估。
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