基于深度学习的千伏切换双能 CT 虚拟单能量图像重建在评估富血管性肝病变中的应用：与标准重建技术的比较。
Deep learning-based reconstruction of virtual monoenergetic images of kVp-switching dual energy CT for evaluation of hypervascular liver lesions: Comparison with standard reconstruction technique.
机构信息
Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea; Center for Medical-IT Convergence Technology Research, Advanced Institutes of Convergence Technology, Suwon, Republic of Korea; Research Institute, ClariPi, Seoul, Republic of Korea.
出版信息
Eur J Radiol. 2022 Sep;154:110390. doi: 10.1016/j.ejrad.2022.110390. Epub 2022 Jun 15.
OBJECTIVE
To investigate clinical applicability of deep learning(DL)-based reconstruction of virtual monoenergetic images(VMIs) of arterial phase liver CT obtained by rapid kVp-switching dual-energy CT for evaluation of hypervascular liver lesions.
MATERIALS AND METHODS
We retrospectively included 109 patients who had available late arterial phase liver CT images of the liver obtained with a rapid switching kVp DECT scanner for suspicious intra-abdominal malignancies. Two VMIs of 70 keV and 40 keV were reconstructed using adaptive statistical iterative reconstruction (ASiR-V) for arterial phase scans. VMIs at 40 keV were additionally reconstructed with a vendor-agnostic DL-based reconstruction technique (ClariCT.AI, ClariPi, DL 40 keV). Qualitative, quantitative image quality and subjective diagnostic acceptability were compared according to reconstruction techniques.
RESULTS
In qualitative analysis, DL 40 keV images showed less image noise (4.55 vs 3.11 vs 3.95, p < 0.001), better image sharpness (4.75 vs 4.16 vs 4.3, p < 0.001), better image contrast (4.98 vs 4.72 vs 4.19, p < 0.017), better lesion conspicuity (4.61 vs 4.23 vs 3.4, p < 0.001) and diagnostic acceptability (4.59 vs 3.88 vs 4.09, p < 0.001) compared with ASiR-V 40 keV or 70 keV image sets. In quantitative analysis, DL 40 keV significantly reduced image noise relative to ASiR-V 40 keV images (49.9%, p < 0.001) and ASiR-V 70 keV images (85.2%, p = 0.012). DL 40 keV images showed significantly higher CNR and SNR than ASiR-V 40 keV image and 70 keV images (p < 0.001).
CONCLUSION
DL-based reconstruction of 40 keV images using vendor-agnostic software showed greater noise reduction, better lesion conspicuity, image contrast, image sharpness, and higher overall image diagnostic acceptability than ASiR for 40 keV or 70 keV images in patients with hypervascular liver lesions.
目的
研究基于深度学习（DL）的虚拟单能量图像（VMIs）在快速 kVp 切换双能 CT 动脉期肝脏 CT 评估肝血管性病变中的临床适用性。
材料与方法
我们回顾性纳入了 109 例因腹部恶性肿瘤可疑而接受快速切换 kVp DECT 扫描仪进行的动脉期肝脏 CT 检查的患者。采用自适应统计迭代重建（ASiR-V）对动脉期扫描分别重建 70keV 和 40keV 的 VMIs。另外，采用一种与供应商无关的基于深度学习的重建技术（ClariCT.AI、ClariPi、DL 40keV）对 40keV 的 VMIs 进行重建。根据重建技术比较图像质量的定性、定量分析和主观诊断可接受性。
结果
在定性分析中，DL 40keV 图像的噪声水平更低（4.55 比 3.11 比 3.95，p<0.001），图像锐利度更好（4.75 比 4.16 比 4.3，p<0.001），图像对比度更好（4.98 比 4.72 比 4.19，p<0.017），病灶显示度更好（4.61 比 4.23 比 3.4，p<0.001），诊断可接受性更好（4.59 比 3.88 比 4.09，p<0.001），与 ASiR-V 40keV 或 70keV 图像组相比。在定量分析中，与 ASiR-V 40keV 图像相比，DL 40keV 图像的噪声降低了 49.9%（p<0.001），与 ASiR-V 70keV 图像相比降低了 85.2%（p=0.012）。DL 40keV 图像的 CNR 和 SNR 显著高于 ASiR-V 40keV 图像和 70keV 图像（p<0.001）。
结论
在患有肝血管性病变的患者中，使用与供应商无关的软件进行基于深度学习的 40keV 图像重建，与 ASiR 用于 40keV 或 70keV 图像相比，可显著降低噪声，提高病灶显示度、图像对比度、图像锐利度和整体图像诊断可接受性。
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