Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China.
Eur Radiol. 2022 Dec;32(12):8140-8151. doi: 10.1007/s00330-022-08870-9. Epub 2022 Jun 24.
To investigate whether deep learning reconstruction (DLR) could keep image quality and reduce radiation dose in interstitial lung disease (ILD) patients compared with HRCT reconstructed with hybrid iterative reconstruction (hybrid-IR).
Seventy ILD patients were prospectively enrolled and underwent HRCT (120 kVp, automatic tube current) and LDCT (120 kVp, 30 mAs) scans. HRCT images were reconstructed with hybrid-IR (Adaptive Iterative Dose Reduction 3-Dimensional [AIDR3D], standard-setting); LDCT images were reconstructed with DLR (Advanced Intelligence Clear-IQ Engine [AiCE], lung/bone, mild/standard/strong setting). Image noise, streak artifact, overall image quality, and visualization of normal and abnormal features of ILD were evaluated.
The mean radiation dose of LDCT was 38% of HRCT. Objective image noise of reconstructed LDCT images was 33.6 to 111.3% of HRCT, and signal-to-noise ratio (SNR) was 0.9 to 3.1 times of the latter (p < 0.001). LDCT-AiCE was not significantly different from or even better than HRCT in overall image quality and visualization of normal lung structures. LDCT-AiCE (lung, mild/standard/strong) showed progressively better recognition of ground glass opacity than HRCT-AIDR3D (p < 0.05, p < 0.01, p < 0.001), and LDCT-AiCE (lung, mild/standard/strong; bone, mild) was superior to HRCT-AIDR3D in visualization of architectural distortion (p < 0.01, p < 0.01, p < 0.01; p < 0.05). LDCT-AiCE (bone, strong) was better than HRCT-AIDR3D in the assessment of bronchiectasis and/or bronchiolectasis (p < 0.05). LDCT-AiCE (bone, mild/standard/strong) was significantly better at the visualization of honeycombing than HRCT-AIDR3D (p < 0.05, p < 0.05, p < 0.01).
Deep learning reconstruction could effectively reduce radiation dose and keep image quality in ILD patients compared to HRCT with hybrid-IR.
• Deep learning reconstruction was a novel image reconstruction algorithm based on deep convolutional neural networks. It was applied in chest CT studies and received auspicious results. • HRCT plays an essential role in the whole process of diagnosis, treatment efficacy evaluation, and follow-ups for interstitial lung disease patients. However, cumulative radiation exposure could increase the risks of cancer. • Deep learning reconstruction method could effectively reduce the radiation dose and keep the image quality compared with HRCT reconstructed with hybrid iterative reconstruction in patients with interstitial lung disease.
研究深度学习重建（DLR）是否能在间质性肺病（ILD）患者中保持图像质量并降低辐射剂量，与混合迭代重建（hybrid-IR）重建的高分辨率 CT（HRCT）相比。
前瞻性纳入 70 名 ILD 患者，行 HRCT（120 kVp，自动管电流）和 LDCT（120 kVp，30 mAs）扫描。HRCT 图像采用混合迭代重建（Adaptive Iterative Dose Reduction 3-Dimensional [AIDR3D]，标准设置）重建；LDCT 图像采用 DLR（Advanced Intelligence Clear-IQ Engine [AiCE]，肺/骨，轻度/标准/强设置）重建。评估图像噪声、条纹伪影、整体图像质量以及ILD 的正常和异常特征的可视化。
LDCT 的平均辐射剂量为 HRCT 的 38%。重建的 LDCT 图像的客观图像噪声为 HRCT 的 33.6%至 111.3%，信噪比（SNR）为后者的 0.9 至 3.1 倍（p < 0.001）。LDCT-AiCE 在整体图像质量和正常肺结构的可视化方面与 HRCT 无显著差异，甚至优于 HRCT。LDCT-AiCE（肺，轻度/标准/强）显示出比 HRCT-AIDR3D 更好的识别磨玻璃混浊的能力（p < 0.05，p < 0.01，p < 0.001），LDCT-AiCE（肺，轻度/标准/强；骨，轻度）在结构扭曲的可视化方面优于 HRCT-AIDR3D（p < 0.01，p < 0.01，p < 0.01；p < 0.05）。LDCT-AiCE（骨，强）在评估支气管扩张和/或支气管扩张方面优于 HRCT-AIDR3D（p < 0.05）。LDCT-AiCE（骨，轻度/标准/强）在蜂窝状结构的可视化方面明显优于 HRCT-AIDR3D（p < 0.05，p < 0.05，p < 0.01）。
与 HRCT 混合迭代重建相比，深度学习重建可有效降低 ILD 患者的辐射剂量并保持图像质量。
深度学习重建：这是一种基于深度卷积神经网络的新型图像重建算法。它已应用于胸部 CT 研究，并取得了良好的效果。
高分辨率 CT：在间质性肺病患者的整个诊断、治疗效果评估和随访过程中起着至关重要的作用。然而，累积的辐射暴露会增加癌症的风险。
深度学习重建方法：与混合迭代重建的 HRCT 相比，在间质性肺病患者中，该方法可有效降低辐射剂量并保持图像质量。
