International Journal of Radiation Research

en Impact of reconstruction algorithms on the success rate and quality of automatic airway segmentation in children under ultra-low-dose chest CT scanning Radiation Biology Radiation Biology تحقيق بديع Original Research <div style="text-align: justify;">Background: To investigate the success rate and quality of automatic airway segmentation using ultra-low dose CT (ULD-CT) images of different reconstruction algorithms. Materials and Methods: Fifty two children who underwent chest ULD-CT were divided into three groups for analysis based on age: group A (n=13, age, 1-2years), group B (n=19, age, 3-6years) and group C (n=20, age, 7-13years). CT images were reconstructed with filtered back-projection (FBP), 50% adaptive statistical iterative reconstruction-Veo (50%ASIR-V), 100%ASIR-V, deep learning image reconstruction (DLIR) with low (DLIR-L), medium (DLIR-M), and high (DLIR-H) strengths. Subjective image quality was evaluated using a 5-point scale. CT value, noise, and sharpness of the trachea were measured. The VCAR software was used to automatically segment airways and reported the total volume. Segmentation success rates were recorded, and segmentation images were subjectively evaluated using a 6-point scale. Results: The average tracheal diameters were 8.53±1.88mm, 10.69±1.65mm, and 12.72±1.97mm, respectively for groups A, B, and C. The segmentation success rate depended on patient groups: group C reached 100%, while group A decreased significantly. In group A, 100%ASIR-V had the lowest rate at 7.69%, while DLIR-M and DLIR-H significantly improved the rate to 38.64% (P=0.03). For the segmented images, DLIR-H provided the lowest noise and highest subjective score while FBP images had the highest noise and 100%ASIR-V had the lowest overall score (P<0.05). There was no significant difference in the total airway volume among the six reconstructions. Conclusion: The airway segmentation success rate in ULD-CT for children depends on the tracheal size. DLIR improves airway segmentation success rate and image quality.</div> CT, pediatrics, deep learning, image processing, computer-assisted. 171 177 http://ijrr.com/browse.php?a_code=A-10-1-1153&slc_lang=en&sid=1 J. Sun 7900319475328460026041 7900319475328460026041 No Department of Radiology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 100045, China H. Li 7900319475328460026042 7900319475328460026042 No Department of Radiology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 100045, China Z. Liu 7900319475328460026043 7900319475328460026043 No Department of Radiology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 100045, China S. Wang 7900319475328460026044 7900319475328460026044 No Department of Medical Imaging, Children's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hospital of Beijing Children’s Hospital, Urumqi 830000, China Y. Peng ppengyun@hotmail.com 7900319475328460026045 7900319475328460026045 Yes Department of Radiology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 100045, China