Ye Kai, Xu Libo, Pan Boyang, Li Jie, Li Meijiao, Yuan Huishu, Gong Nan-Jie
Department of Radiology, Peking University Third Hospital, Beijing, China.
Laboratory for Intelligent Medical Imaging, Tsinghua Cross-strait Research Institute, Xiamen, China.
Eur Radiol. 2025 Jan 10. doi: 10.1007/s00330-024-11317-y.
To evaluate the image quality and lung nodule detectability of ultralow-dose CT (ULDCT) with adaptive statistical iterative reconstruction-V (ASiR-V) post-processed using a deep learning image reconstruction (DLIR)-based image domain compared to low-dose CT (LDCT) and ULDCT without DLIR.
A total of 210 patients undergoing lung cancer screening underwent LDCT (mean ± SD, 0.81 ± 0.28 mSv) and ULDCT (0.17 ± 0.03 mSv) scans. ULDCT images were reconstructed with ASiR-V (ULDCT-ASiR-V) and post-processed using DLIR (ULDCT-DLIR). The quality of the three CT images was analyzed. Three radiologists detected and measured pulmonary nodules on all CT images, with LDCT results serving as references. Nodule conspicuity was assessed using a five-point Likert scale, followed by further statistical analyses.
A total of 463 nodules were detected using LDCT. The image noise of ULDCT-DLIR decreased by 60% compared to that of ULDCT-ASiR-V and was lower than that of LDCT (p < 0.001). The subjective image quality scores for ULDCT-DLIR (4.4 [4.1, 4.6]) were also higher than those for ULDCT-ASiR-V (3.6 [3.1, 3.9]) (p < 0.001). The overall nodule detection rates for ULDCT-ASiR-V and ULDCT-DLIR were 82.1% (380/463) and 87.0% (403/463), respectively (p < 0.001). The percentage difference between diameters > 1 mm was 2.9% (ULDCT-ASiR-V vs. LDCT) and 0.5% (ULDCT-DLIR vs. LDCT) (p = 0.009). Scores of nodule imaging sharpness on ULDCT-DLIR (4.0 ± 0.68) were significantly higher than those on ULDCT-ASiR-V (3.2 ± 0.50) (p < 0.001).
DLIR-based image domain improves image quality, nodule detection rate, nodule imaging sharpness, and nodule measurement accuracy of ASiR-V on ULDCT.
Question Deep learning post-processing is simple and cheap compared with raw data processing, but its performance is not clear on ultralow-dose CT. Findings Deep learning post-processing enhanced image quality and improved the nodule detection rate and accuracy of nodule measurement of ultralow-dose CT. Clinical relevance Deep learning post-processing improves the practicability of ultralow-dose CT and makes it possible for patients with less radiation exposure during lung cancer screening.
评估基于深度学习图像重建（DLIR）的图像域对自适应统计迭代重建-V（ASiR-V）后处理的超低剂量CT（ULDCT）的图像质量和肺结节可检测性，并与低剂量CT（LDCT）和无DLIR的ULDCT进行比较。
共有210例接受肺癌筛查的患者接受了LDCT（平均±标准差，0.81±0.28 mSv）和ULDCT（0.17±0.03 mSv）扫描。ULDCT图像采用ASiR-V重建（ULDCT-ASiR-V）并使用DLIR进行后处理（ULDCT-DLIR）。分析了三种CT图像的质量。三名放射科医生在所有CT图像上检测并测量肺结节，以LDCT结果作为参考。使用五点李克特量表评估结节的清晰度，随后进行进一步的统计分析。
使用LDCT共检测到463个结节。与ULDCT-ASiR-V相比，ULDCT-DLIR的图像噪声降低了60%，且低于LDCT（p<0.001）。ULDCT-DLIR的主观图像质量评分（4.4[4.1,4.6]）也高于ULDCT-ASiR-V（3.6[3.1,3.9]）（p<0.001）。ULDCT-ASiR-V和ULDCT-DLIR的总体结节检测率分别为82.1%（380/463）和87.0%（403/463）（p<0.001）。直径>1 mm的差异百分比在ULDCT-ASiR-V与LDCT之间为2.9%，在ULDCT-DLIR与LDCT之间为0.5%（p=0.009）。ULDCT-DLIR上结节成像清晰度的评分（4.0±0.68）显著高于ULDCT-ASiR-V上的评分（3.2±
