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Abstract：
Our objective was to develop and evaluate the clinical feasibility of deep-learning-based synthetic contrast-enhanced computed tomography (DL-SynCCT) in patients designated for nonenhanced CT (NECT). We proposed a weakly supervised learning with the utilization of virtual non-contrast CT (VNC) for the development of DL-SynCCT. Training and internal validations were performed with 2202 pairs of retrospectively collected contrast-enhanced CT (CECT) images with the corresponding VNC images acquired from dual-energy CT. Clinical validation was performed using an external validation set including 398 patients designated for true nonenhanced CT (NECT), from multiple vendors at three institutes. Detection of lesions was performed by three radiologists with only NECT in the first session and an additionally provided DL-SynCCT in the second session. The mean peak signal-to-noise ratio (PSNR) and structural similarity index map (SSIM) of the DL-SynCCT compared to CECT were 43.25 ± 0.41 and 0.92 ± 0.01, respectively. With DL-SynCCT, the pooled sensitivity for lesion detection (72.0% to 76.4%, P < 0.001) and level of diagnostic confidence (3.0 to 3.6, P < 0.001) significantly increased. In conclusion, DL-SynCCT generated by weakly supervised learning showed significant benefit in terms of sensitivity in detecting abnormal findings when added to NECT in patients designated for nonenhanced CT scans.
摘要：
我们的目标是开发和评估基于深度学习的合成对比增强计算机断层扫描（DL-SynCCT）在指定为非增强CT（NECT）的患者中的临床可行性。我们提出了一种弱监督学习，利用虚拟非对比CT（VNC）来开发DL-SynCCT。使用2202对回顾性收集的对比增强CT（CECT）图像以及从双能CT获取的相应VNC图像进行训练和内部验证。使用外部验证集进行临床验证，包括398名指定为真正非增强CT（NECT）的患者，来自三个研究所的多个供应商。由三名放射科医师在第一疗程中仅使用NECT并在第二疗程中另外提供DL-SynCCT进行病变检测。与CECT相比，DL-SynCCT的平均峰值信噪比（PSNR）和结构相似性指数图（SSIM）分别为43.25±0.41和0.92±0.01。使用DL-SynCCT，病变检测的合并灵敏度(72.0%至76.4%，P<0.001）和诊断置信度（3.0至3.6，P<0.001）显着增加。总之,在指定进行非增强CT扫描的患者中，将弱监督学习产生的DL-SynCCT添加到NECT中，在检测异常发现的敏感性方面显示出明显的优势。
