Department of Radiology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Yangsan 50612, Korea.
Department of Internal Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan 49241, Korea.
Medicina (Kaunas). 2022 Jul 15;58(7):939. doi: 10.3390/medicina58070939.
Background and Objectives: Although reducing the radiation dose level is important during diagnostic computed tomography (CT) applications, effective image quality enhancement strategies are crucial to compensate for the degradation that is caused by a dose reduction. We performed this prospective study to quantify emphysema on ultra-low-dose CT images that were reconstructed using deep learning-based image reconstruction (DLIR) algorithms, and compared and evaluated the accuracies of DLIR algorithms versus standard-dose CT. Materials and Methods: A total of 32 patients were prospectively enrolled, and all underwent standard-dose and ultra-low-dose (120 kVp; CTDIvol < 0.7 mGy) chest CT scans at the same time in a single examination. A total of six image datasets (filtered back projection (FBP) for standard-dose CT, and FBP, adaptive statistical iterative reconstruction (ASIR-V) 50%, DLIR-low, DLIR-medium, DLIR-high for ultra-low-dose CT) were reconstructed for each patient. Image noise values, emphysema indices, total lung volumes, and mean lung attenuations were measured in the six image datasets and compared (one-way repeated measures ANOVA). Results: The mean effective doses for standard-dose and ultra-low-dose CT scans were 3.43 ± 0.57 mSv and 0.39 ± 0.03 mSv, respectively (p < 0.001). The total lung volume and mean lung attenuation of five image datasets of ultra-low-dose CT scans, emphysema indices of ultra-low-dose CT scans reconstructed using ASIR-V 50 or DLIR-low, and the image noise of ultra-low-dose CT scans that were reconstructed using DLIR-low were not different from those of standard-dose CT scans. Conclusions: Ultra-low-dose CT images that were reconstructed using DLIR-low were found to be useful for emphysema quantification at a radiation dose of only 11% of that required for standard-dose CT.
在诊断性计算机断层扫描（CT）应用中，降低辐射剂量水平很重要，但有效增强图像质量的策略对于补偿因剂量降低而导致的图像质量下降至关重要。我们进行了这项前瞻性研究，旨在量化使用基于深度学习的图像重建（DLIR）算法重建的超低剂量 CT 图像中的肺气肿，并比较和评估 DLIR 算法与标准剂量 CT 的准确性。
共前瞻性纳入 32 例患者，所有患者均在单次检查中同时进行标准剂量和超低剂量（120 kVp；CTDIvol < 0.7 mGy）胸部 CT 扫描。为每位患者重建了 6 个图像数据集（标准剂量 CT 的滤波反投影（FBP），超低剂量 CT 的 FBP、自适应统计迭代重建（ASIR-V）50%、DLIR-低、DLIR-中、DLIR-高）。在 6 个图像数据集中测量了图像噪声值、肺气肿指数、全肺容积和平均肺衰减，并进行了比较（单因素重复测量方差分析）。
标准剂量和超低剂量 CT 扫描的平均有效剂量分别为 3.43 ± 0.57 mSv 和 0.39 ± 0.03 mSv（p < 0.001）。超低剂量 CT 扫描的 5 个图像数据集的全肺容积和平均肺衰减、ASIR-V 50 或 DLIR-低重建的超低剂量 CT 扫描的肺气肿指数以及 DLIR-低重建的超低剂量 CT 扫描的图像噪声与标准剂量 CT 扫描无差异。
使用 DLIR-低重建的超低剂量 CT 图像在仅为标准剂量 CT 所需辐射剂量的 11%时可用于肺气肿定量。
