使用深度学习重建降低低剂量胸部 CT 辐射剂量 75%。
75% radiation dose reduction using deep learning reconstruction on low-dose chest CT.
机构信息
Department of Radiology, Seoul National University Hospital and College of Medicine, Seoul, 03080, Republic of Korea.
Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 08826, Republic of Korea.
出版信息
BMC Med Imaging. 2023 Sep 11;23(1):121. doi: 10.1186/s12880-023-01081-8.
OBJECTIVE
Few studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to compare the image quality and lung nodule detectability between chest CT using a quarter of the low dose (QLD) reconstructed with vendor-agnostic deep-learning image reconstruction (DLIR) and conventional low-dose (LD) CT reconstructed with iterative reconstruction (IR).
MATERIALS AND METHODS
We retrospectively collected 100 patients (median age, 61 years [IQR, 53-70 years]) who received LDCT using a dual-source scanner, where total radiation was split into a 1:3 ratio. QLD CT was generated using a quarter dose and reconstructed with DLIR (QLD-DLIR), while LDCT images were generated using a full dose and reconstructed with IR (LD-IR). Three thoracic radiologists reviewed subjective noise, spatial resolution, and overall image quality, and image noise was measured in five areas. The radiologists were also asked to detect all Lung-RADS category 3 or 4 nodules, and their performance was evaluated using area under the jackknife free-response receiver operating characteristic curve (AUFROC).
RESULTS
The median effective dose was 0.16 (IQR, 0.14-0.18) mSv for QLD CT and 0.65 (IQR, 0.57-0.71) mSv for LDCT. The radiologists' evaluations showed no significant differences in subjective noise (QLD-DLIR vs. LD-IR, lung-window setting; 3.23 ± 0.19 vs. 3.27 ± 0.22; P = .11), spatial resolution (3.14 ± 0.28 vs. 3.16 ± 0.27; P = .12), and overall image quality (3.14 ± 0.21 vs. 3.17 ± 0.17; P = .15). QLD-DLIR demonstrated lower measured noise than LD-IR in most areas (P < .001 for all). No significant difference was found between QLD-DLIR and LD-IR for the sensitivity (76.4% vs. 72.2%; P = .35) or the AUFROCs (0.77 vs. 0.78; P = .68) in detecting Lung-RADS category 3 or 4 nodules. Under a noninferiority limit of -0.1, QLD-DLIR showed noninferior detection performance (95% CI for AUFROC difference, -0.04 to 0.06).
CONCLUSION
QLD-DLIR images showed comparable image quality and noninferior nodule detectability relative to LD-IR images.
目的
很少有研究探讨使用深度学习重建来降低 CT 辐射剂量的临床可行性。我们旨在比较使用四分之一低剂量（QLD）进行重建的与使用迭代重建（IR）进行重建的供应商不可知的深度学习图像重建（DLIR）的胸部 CT 与常规低剂量（LD）CT 的图像质量和肺结节检测能力。
材料和方法
我们回顾性收集了 100 名接受双源扫描仪进行 LDCT 的患者（中位年龄 61 岁[IQR，53-70 岁]），总辐射量分为 1:3 的比例。QLD CT 使用四分之一剂量生成，并使用 DLIR 进行重建（QLD-DLIR），而 LDCT 图像则使用全剂量生成并使用 IR 进行重建（LD-IR）。三名胸部放射科医生评估了主观噪声、空间分辨率和整体图像质量，并在五个区域测量图像噪声。放射科医生还被要求检测所有 Lung-RADS 3 或 4 类结节，并使用 Jackknife 自由响应接收器操作特征曲线下面积（AUFROC）评估他们的表现。
结果
QLD CT 的中位有效剂量为 0.16（IQR，0.14-0.18）mSv，LDCT 的中位有效剂量为 0.65（IQR，0.57-0.71）mSv。放射科医生的评估显示，在主观噪声（QLD-DLIR 与 LD-IR，肺窗设置；3.23±0.19 与 3.27±0.22；P=0.11）、空间分辨率（3.14±0.28 与 3.16±0.27；P=0.12）和整体图像质量（3.14±0.21 与 3.17±0.17；P=0.15）方面无显著差异。在大多数区域，QLD-DLIR 显示的测量噪声均低于 LD-IR（所有 P<0.001）。在检测 Lung-RADS 3 或 4 类结节的敏感性（76.4%与 72.2%；P=0.35）或 AUFROCs（0.77 与 0.78；P=0.68）方面，QLD-DLIR 与 LD-IR 之间无显著差异。在非劣效性界限为-0.1 的情况下，QLD-DLIR 显示出非劣效的检测性能（AUFROC 差异的 95%置信区间为-0.04 至 0.06）。
结论
与 LD-IR 图像相比，QLD-DLIR 图像显示出相当的图像质量和非劣效的结节检测能力。
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