Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
Department of Radiology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
Sci Rep. 2021 Oct 14;11(1):20403. doi: 10.1038/s41598-021-00058-3.
This study aimed to evaluate a deep learning model for generating synthetic contrast-enhanced CT (sCECT) from non-contrast chest CT (NCCT). A deep learning model was applied to generate sCECT from NCCT. We collected three separate data sets, the development set (n = 25) for model training and tuning, test set 1 (n = 25) for technical evaluation, and test set 2 (n = 12) for clinical utility evaluation. In test set 1, image similarity metrics were calculated. In test set 2, the lesion contrast-to-noise ratio of the mediastinal lymph nodes was measured, and an observer study was conducted to compare lesion conspicuity. Comparisons were performed using the paired t-test or Wilcoxon signed-rank test. In test set 1, sCECT showed a lower mean absolute error (41.72 vs 48.74; P < .001), higher peak signal-to-noise ratio (17.44 vs 15.97; P < .001), higher multiscale structural similarity index measurement (0.84 vs 0.81; P < .001), and lower learned perceptual image patch similarity metric (0.14 vs 0.15; P < .001) than NCCT. In test set 2, the contrast-to-noise ratio of the mediastinal lymph nodes was higher in the sCECT group than in the NCCT group (6.15 ± 5.18 vs 0.74 ± 0.69; P < .001). The observer study showed for all reviewers higher lesion conspicuity in NCCT with sCECT than in NCCT alone (P ≤ .001). Synthetic CECT generated from NCCT improves the depiction of mediastinal lymph nodes.
本研究旨在评估一种从非对比胸部 CT（NCCT）生成合成对比增强 CT（sCECT）的深度学习模型。将深度学习模型应用于从 NCCT 生成 sCECT。我们收集了三个独立的数据集，开发集（n=25）用于模型训练和调整，测试集 1（n=25）用于技术评估，测试集 2（n=12）用于临床实用性评估。在测试集 1 中，计算了图像相似性度量。在测试集 2 中，测量了纵隔淋巴结的病灶对比噪声比，并进行了观察者研究以比较病灶的显著性。使用配对 t 检验或 Wilcoxon 符号秩检验进行比较。在测试集 1 中，sCECT 的平均绝对误差较低（41.72 对 48.74；P<0.001），峰值信噪比较高（17.44 对 15.97；P<0.001），多尺度结构相似性指数测量值较高（0.84 对 0.81；P<0.001），而学习感知图像补丁相似性度量值较低（0.14 对 0.15；P<0.001）比 NCCT。在测试集 2 中，纵隔淋巴结的对比噪声比在 sCECT 组高于 NCCT 组（6.15±5.18 对 0.74±0.69；P<0.001）。观察者研究表明，对于所有审阅者，NCCT 加 sCECT 的病灶显著性均高于 NCCT 单独（P≤0.001）。从 NCCT 生成的合成 CECT 可改善纵隔淋巴结的描绘。
