Image quality and radiation dose of reduced-dose abdominopelvic computed tomography (CT) with silver filter and deep learning reconstruction.

Journal: Scientific reports
Published Date:

Abstract

To assess the image quality and radiation dose between reduced-dose CT with deep learning reconstruction (DLR) using SilverBeam filter and standard dose with iterative reconstruction (IR) in abdominopelvic CT. In total, 182 patients (mean age ± standard deviation, 63 ± 14 years; 100 men) were included. Standard-dose scanning was performed with a tube voltage of 100 kVp, automatic tube current modulation, and IR reconstruction, whereas reduced-dose scanning was performed with a tube voltage of 120 kVp, a SilverBeam filter, and DLR. Additionally, a contrast-enhanced (CE)-boost image was obtained for reduced-dose scanning. Radiation dose, objective, and subjective image analyses were performed in each body mass index (BMI) category. The radiation dose for SilverBeam with DLR was significantly lower than that of standard dose with IR, with an average reduction in the effective dose of 59.0% (1.87 vs. 4.57 mSv). Standard dose with IR (10.59 ± 1.75) and SilverBeam with DLR (10.60 ± 1.08) showed no significant difference in image noise (p = 0.99). In the obese group (BMI > 25 kg/m), there were no significant differences in SNRs of the liver, pancreas, and spleen between standard dose with IR and SilverBeam with DLR. SilverBeam with DLR + CE-boost demonstrated significantly better SNRs and CNRs, compared with standard dose with IR and SilverBeam with DLR. DLR combined with silver filter is effective for routine abdominopelvic CT, achieving a clearly reduced radiation dose while providing image quality that is non-inferior to standard dose with IR.

Authors

  • Chuluunbaatar Otgonbaatar
    Department of Radiology, College of Medicine, Seoul National University, 03080 Seoul, Republic of Korea.
  • Sang-Hyun Jeon
    Department of Radiology, Wonju Severance Christian Hospital, Wonju College of Medicine, Yonsei University of Korea, Wonju 26426, Republic of Korea.
  • Sung-Jin Cha
    Department of Radiology, Wonju Severance Christian Hospital, Wonju College of Medicine, Yonsei University of Korea, Wonju 26426, Republic of Korea.
  • Hackjoon Shim
    Connect AI Research Center, Yonsei University College of Medicine, 03772 Seoul, Republic of Korea.
  • Jin Woo Kim
    Department of Food Science, Sun Moon University, Natural Science 118, 70 Sunmoon-ro 221, Tangjeong-myeon, Asan-si 336-708, Korea.
  • Jhii-Hyun Ahn
    Department of Radiology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, 20 Ilsan-ro, Wonju, Gangwon-do, 26426, Republic of Korea. radajh@yonsei.ac.kr.