Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, 45147, Essen, Germany.
Department of Surgery and Orthopedics, Landesspital Liechtenstein, Heiligkreuz 25, 9490, Vaduz, Liechtenstein.
Sci Rep. 2021 May 13;11(1):10215. doi: 10.1038/s41598-021-89647-w.
For CT pulmonary angiograms, a scout view obtained in anterior-posterior projection is usually used for planning. For bolus tracking the radiographer manually locates a position in the CT scout view where the pulmonary trunk will be visible in an axial CT pre-scan. We automate the task of localizing the pulmonary trunk in CT scout views by deep learning methods. In 620 eligible CT scout views of 563 patients between March 2003 and February 2020 the region of the pulmonary trunk as well as an optimal slice ("reference standard") for bolus tracking, in which the pulmonary trunk was clearly visible, was annotated and used to train a U-Net predicting the region of the pulmonary trunk in the CT scout view. The networks' performance was subsequently evaluated on 239 CT scout views from 213 patients and was compared with the annotations of three radiographers. The network was able to localize the region of the pulmonary trunk with high accuracy, yielding an accuracy of 97.5% of localizing a slice in the region of the pulmonary trunk on the validation cohort. On average, the selected position had a distance of 5.3 mm from the reference standard. Compared to radiographers, using a non-inferiority test (one-sided, paired Wilcoxon rank-sum test) the network performed as well as each radiographer (P < 0.001 in all cases). Automated localization of the region of the pulmonary trunk in CT scout views is possible with high accuracy and is non-inferior to three radiographers.
对于 CT 肺动脉造影，通常在前后位投影中获取的扫描图像用于规划。对于团注追踪，放射技师手动在 CT 扫描的轴位预扫描中定位肺动脉可见的 CT 扫描图像位置。我们使用深度学习方法自动定位 CT 扫描图像中的肺动脉主干。在 2003 年 3 月至 2020 年 2 月期间的 563 名患者的 620 份合格 CT 扫描图像中，对肺动脉主干区域以及用于团注追踪的最佳切片（“参考标准”）进行了标注，并将其用于训练 U-Net 预测 CT 扫描图像中的肺动脉主干区域。随后在 213 名患者的 239 份 CT 扫描图像上评估了网络的性能，并将其与三位放射技师的标注进行了比较。该网络能够非常准确地定位肺动脉主干区域，在验证队列上定位肺动脉主干区域的切片的准确率达到 97.5%。平均而言，所选位置与参考标准的距离为 5.3 毫米。与放射技师相比，使用非劣效性检验（单侧、配对 Wilcoxon 秩和检验），网络的表现与每位放射技师相当（在所有情况下 P < 0.001）。使用深度学习方法自动定位 CT 扫描图像中的肺动脉主干区域是可行的，其准确率与三位放射技师相当。
