Fully automated accurate patient positioning in computed tomography using anterior–posterior localizer images and a deep neural network: a dual-center study

• Published in: European radiology 2023-05, Vol.33 (5), p.3243-3252
• Main Authors: Salimi, Yazdan, Shiri, Isaac, Akavanallaf, Azadeh, Mansouri, Zahra, Arabi, Hossein, Zaidi, Habib
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial neural networks; Cameras; Computed Tomography; Consoles; Deep learning; Diagnostic Radiology; Errors; Humans; Image degradation; Image Processing, Computer-Assisted - methods; Image quality; Imaging; Imaging, Three-Dimensional; Internal Medicine; Interventional Radiology; Machine learning; Medical imaging; Medicine; Medicine & Public Health; Neural networks; Neural Networks, Computer; Neuroradiology; Patient positioning; Patient Positioning - methods; Performance evaluation; Radiation; Radiation dosage; Radiology; Tomography, X-Ray Computed - methods; Ultrasound; Visibility
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-023-09424-3

Objectives This study aimed to improve patient positioning accuracy by relying on a CT localizer and a deep neural network to optimize image quality and radiation dose. Methods We included 5754 chest CT axial and anterior–posterior (AP) images from two different centers, C1 and C2. After pre-processing, images were split into training (80%) and test (20%) datasets. A deep neural network was trained to generate 3D axial images from the AP localizer. The geometric centerlines of patient bodies were indicated by creating a bounding box on the predicted images. The distance between the body centerline, estimated by the deep learning model and ground truth (BCAP), was compared with patient mis-centering during manual positioning (BCMP). We evaluated the performance of our model in terms of distance between the lung centerline estimated by the deep learning model and the ground truth (LCAP). Results The error in terms of BCAP was − 0.75 ± 7.73 mm and 2.06 ± 10.61 mm for C1 and C2, respectively. This error was significantly lower than BCMP, which achieved an error of 9.35 ± 14.94 and 13.98 ± 14.5 mm for C1 and C2, respectively. The absolute BCAP was 5.7 ± 5.26 and 8.26 ± 6.96 mm for C1 and C2, respectively. The LCAP metric was 1.56 ± 10.8 and −0.27 ± 16.29 mm for C1 and C2, respectively. The error in terms of BCAP and LCAP was higher for larger patients ( p value