超低剂量CT结合超分辨率深度学习重建用于恶性肿瘤转移筛查
该前瞻性研究纳入271例癌症监测患者，行增强全身超低剂量CT，分别用混合迭代重建（HIR）、常规分辨率深度学习重建（NR-DLR）和超分辨率深度学习重建（SR-DLR）重建图像。SR-DLR较HIR和NR-DLR显著提升图像质量、降低噪声，对原发癌和转移灶的检出率更高，且辐射剂量较标准剂量CT降低约70%。
要点
- SR-DLR可在降低约70%辐射剂量的超低剂量CT下保持优良图像质量和转移灶检出能力。
- SR-DLR较HIR和NR-DLR显著改善整体图像质量、锐利度和噪声，且不改变各器官CT值。
- SR-DLR对17种原发癌和178处转移灶的检出率优于HIR和NR-DLR，对良性病变检出率接近100%。
关键影像表现/征象
- SR-DLR较HIR和NR-DLR显著改善整体图像质量、锐利度和图像噪声（p < 0.001）。
- 三种重建算法测量的所有器官平均衰减值无显著差异（p > 0.05）。
- SR-DLR较HIR显著降低定量图像噪声约50%（p ≤ 0.004），并显著提高所有器官的SNR（p < 0.001），不改变衰减值。
- SR-DLR对17种原发癌和178处转移灶的检出率优于HIR和NR-DLR。
- SR-DLR对9种良性病变（n = 1,264）的检出率接近100%。
诊断标准与关键数值
- 超低剂量CT较既往标准剂量CT：容积CT剂量指数降低71.1%，剂量长度乘积降低70.2%，有效剂量降低70.6%，体型特异性剂量估计降低70.9%。
- SR-DLR较HIR定量图像噪声降低约50%（p ≤ 0.004）。
临床应用与阅片要点
- 在癌症监测中可采用SR-DLR重建超低剂量CT，以大幅降低辐射剂量并保持转移灶检出能力。
- SR-DLR不改变组织衰减值，可安全用于基于CT值的定性判断。
- 对原发癌和转移灶的检出，SR-DLR均优于HIR和NR-DLR，阅片时优先选用SR-DLR图像。
以上解读依据论文摘要（全文未开放获取或许可不允许翻译转载）。
英文原文（摘要）· 点击展开
PURPOSE: To evaluate the feasibility and diagnostic performance of ultra-low-dose CT (ULD-CT) for screening malignant metastasis using super-resolution deep learning reconstruction (SR-DLR) compared with normal-resolution DLR (NR-DLR) and hybrid iterative reconstruction (HIR).
MATERIALS AND METHODS: Patients with cancer undergoing surveillance were enrolled prospectively and underwent contrast-enhanced whole-body ULD-CT. The images were reconstructed using HIR, NR-DLR, and SR-DLR. Two radiologists independently evaluated image quality, lesion detectability, and diagnostic performance. Radiation dose metrics were compared with prior standard-dose CT.
RESULTS: Of the 271 patients (mean age 66.3 ± 12.2 years; 134 men), 56 (20.7%) had disease requiring therapeutic intervention. The ULD-CT images reduced the volume CT dose index by 71.1%, dose-length product by 70.2%, effective dose by 70.6%, and size-specific dose estimate by 70.9%. SR-DLR yielded significant improvement in the overall image quality, sharpness, and image noise reduction compared with HIR and NR-DLR (p < 0.001). The mean attenuation values measured in all organs did not differ significantly among the three reconstruction algorithms (p > 0.05). SR-DLR significantly reduced quantitative image noise by 50% compared with HIR (p ≤ 0.004) and significantly improved SNR across all organs (p < 0.001), without altering attenuation values. SR-DLR demonstrated superior detection rates for malignant lesions for 17 primary cancers and 178 metastatic lesions compared with HIR and NR-DLR. For benign lesions, SR-DLR achieved a nearly 100% detection rate across 9 lesion types (n = 1,264).
CONCLUSIONS: SR-DLR enables approximately 70% radiation dose reduction, while maintaining superior image quality and high diagnostic performance for detection of visceral and soft-tissue metastatic disease in oncological patients undergoing cancer surveillance.
引用
- Ozaki K, Iguchi R, Hasegawa H, et al. Ultra-low-dose CT for malignant metastasis screening using a deep learning image reconstruction algorithm. Eur J Radiol. 2026. PMID: 42102570. doi:10.1016/j.ejrad.2026.112896
供执业医师工作参考，不替代临床判断；数值与分级以指南原文为准。