000K utf8 1100 2021$c2021-05-13 1500 eng 2050 urn:nbn:de:hbz:465-20240902-120358-7 2051 10.1038/s41598-021-89647-w 3000 Demircioğlu, Aydin 3010 Blex, Sebastian 3010 Geske, Henrike 3010 Kim, Moon-Sung 3010 Nassenstein, Kai 3010 Quinsten, Anton S. 3010 Stein, Magdalena Charis 3010 Umutlu, Lale 4000 Detecting the pulmonary trunk in CT scout views using deep learning [Demircioğlu, Aydin] 4209 For CT pulmonary angiograms, a scout view obtained in anterior–posterior projection is usually used for planning. For bolus tracking the radiographer manually locates a position in the CT scout view where the pulmonary trunk will be visible in an axial CT pre-scan. We automate the task of localizing the pulmonary trunk in CT scout views by deep learning methods. In 620 eligible CT scout views of 563 patients between March 2003 and February 2020 the region of the pulmonary trunk as well as an optimal slice (“reference standard”) for bolus tracking, in which the pulmonary trunk was clearly visible, was annotated and used to train a U-Net predicting the region of the pulmonary trunk in the CT scout view. The networks’ performance was subsequently evaluated on 239 CT scout views from 213 patients and was compared with the annotations of three radiographers. The network was able to localize the region of the pulmonary trunk with high accuracy, yielding an accuracy of 97.5% of localizing a slice in the region of the pulmonary trunk on the validation cohort. On average, the selected position had a distance of 5.3 mm from the reference standard. Compared to radiographers, using a non-inferiority test (one-sided, paired Wilcoxon rank-sum test) the network performed as well as each radiographer (P < 0.001 in all cases). Automated localization of the region of the pulmonary trunk in CT scout views is possible with high accuracy and is non-inferior to three radiographers. 4950 https://doi.org/10.1038/s41598-021-89647-w$xR$3Volltext$534 4950 https://nbn-resolving.org/urn:nbn:de:hbz:465-20240902-120358-7$xR$3Volltext$534 4961 https://duepublico2.uni-due.de/receive/duepublico_mods_00077940 5051 610 5550 Computed tomography 5550 Data acquisition 5550 Machine learning