We proposed a deep neural network for automatic patient positioning using only the CT image localizer, achieving a performance comparable to alternative techniques, such as the external 3D visual camera.Patient mis-centering in the anterior–posterior direction (AP) is a common problem in clinical practice which can degrade image quality and increase patient radiation dose.The accuracy of the proposed method was comparable to available alternative methods, carrying the advantage of being free from errors related to objects blocking the camera visibility. The error in terms of BCAP and LCAP was higher for larger patients ( p value < 0.01). The LCAP metric was 1.56 ± 10.8 and −0.27 ± 16.29 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. 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 error in terms of BCAP was − 0.75 ± 7.73 mm and 2.06 ± 10.61 mm for C1 and C2, respectively. 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). 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). The geometric centerlines of patient bodies were indicated by creating a bounding box on the predicted images. A deep neural network was trained to generate 3D axial images from the AP localizer. After pre-processing, images were split into training (80%) and test (20%) datasets. We included 5754 chest CT axial and anterior–posterior (AP) images from two different centers, C1 and C2. 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.
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