Department of Radiology, Amiens University Hospital, 1 Rond-Point du Professeur Christian Cabrol, F-80054 Amiens Cedex 01, France.
Biophysics and Image Processing Unit, Amiens University Hospital, Amiens, France.
Eur J Radiol. 2022 Jul;152:110338. doi: 10.1016/j.ejrad.2022.110338. Epub 2022 May 5.
Quantitative analysis of emphysema volume is affected by the radiation dose and the CT reconstruction technique. We aim to evaluate the influence of a commercially available deep learning image reconstruction algorithm (DLIR) on the quantification of pulmonary emphysema in low-dose chest CT.
We performed a retrospective study of low dose chest CT scans in 54 patients with chronic obstructive pulmonary disease (COPD). Raw data were reconstructed using FBP, iterative reconstruction (ASIR-V 70%) and deep learning based algorithms at high, medium and low-strength (DLIR -H, -M, -L). Filtered FBP images served as reference. Pulmonary emphysema volume (proportion of voxels below -950 UH) was measured on each reconstruction dataset and visually assessed by a chest radiologist. Quantitative image quality was assessed by placing 3 regions of interest in the trachea, in air and in a paraspinal muscle. Signal to noise ratio was also measured.
The mean CDTIvol was 2.38 ± 0.68 mGy. Significant differences in emphysema volumes between the filtered FBP reference and ASIR-V, DLIR-H, DLIR-M or DLIR-L were observed, (p < 10) for all. A strong correlation between filtered FBP volumes and DLIR-H was reported (r = 0.999, p < 10), a 10% overestimation with DLIR-H being observed. Noise was significantly reduced in DLIR-H volumes compared to the other reconstruction methods. Signal to noise ratio was improved when using DLIR-H (p < 10).
There are significant differences regarding emphysema volumes between FBP, iterative reconstruction or deep learning-based DLIR algorithm. DLIR-H shows the closest correlation to filtered FBP while increasing SNR.
肺气肿体积的定量分析受辐射剂量和 CT 重建技术的影响。我们旨在评估一种商用深度学习图像重建算法（DLIR）对低剂量胸部 CT 中肺肺气肿定量的影响。
我们对 54 例慢性阻塞性肺疾病（COPD）患者的低剂量胸部 CT 扫描进行了回顾性研究。原始数据使用 FBP、迭代重建（ASIR-V 70%）和高、中、低强度的深度学习算法（DLIR-H、-M、-L）进行重建。滤波 FBP 图像作为参考。在每个重建数据集上测量肺气肿体积（低于-950 UH 的体素比例），并由胸部放射科医生进行视觉评估。通过在气管、空气和脊柱旁肌肉中放置 3 个感兴趣区域来评估图像质量。还测量了信噪比。
平均 CDTIvol 为 2.38 ± 0.68 mGy。在滤波 FBP 参考与 ASIR-V、DLIR-H、DLIR-M 或 DLIR-L 之间，观察到肺气肿体积存在显著差异（p < 10）。报告了滤波 FBP 体积与 DLIR-H 之间的强相关性（r = 0.999，p < 10），观察到 DLIR-H 有 10%的高估。与其他重建方法相比，DLIR-H 中的噪声显著降低。使用 DLIR-H 时，信噪比得到提高（p < 10）。
在 FBP、迭代重建或基于深度学习的 DLIR 算法之间，肺气肿体积存在显著差异。DLIR-H 与滤波 FBP 具有最密切的相关性，同时提高了 SNR。
