[The application value of deep learning image reconstruction on improving image quality and evaluating the Qanadli embolism index of dual low-dose CT pulmonary angiography].

Journal: Zhonghua yi xue za zhi
Published Date:

Abstract

To compare the image quality and Qanadli embolism index between deep learning image reconstruction (DLR) and adaptive statistical iterative reconstruction-veo (ASiR-V) in dual low-dose CT pulmonary angiography (CTPA) with low contrast agent dose and low radiation dose. Eighty-eight patients who underwent dual low-dose CTPA in the radiology department of the affiliated hospital of Xuzhou Medical University from October 2020 to March 2021 were retrospectively analyzed, including 44 males and 44 females, aged from 11 to 87 years (61±15 years). The CTPA examination were performed using 80 kV tube voltage and 20 ml contrast agent. The raw data were reconstructed using standard kernel DLR high level (DL-H) and ASiR-V reconstruction, respectively. The patients were divided into standard kernel DL-H group (=88, 33 cases of positive embolism) and ASiR-V group (=88, 36 cases of positive embolism). The CT value, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), subjective image quality score, Qanadli embolism index, positive rate and positive Qanadli embolism index were compared between the two groups. There were no statistically significant differences in CT values of the main pulmonary artery, the right pulmonary artery and the left pulmonary artery between the standard kernel DL-H group and ASiR-V group [(405.8±111.7) vs (404.0±112.0) HU, (412.9±113.1) vs (411.5±112.2) HU, (418.1±119.9) vs (415.4±118.0) HU, respectively;all >0.05)]. The image noise of the main pulmonary artery, the right pulmonary artery and the left pulmonary artery in the standard kernel DL-H group was significantly lower than the ASiR-V group(16.6±4.7 vs 28.1±4.8, 18.3±6.1 vs 29.8±4.9, 17.6±5.6 vs 28.4±4.7, respectively;all <0.001). The SNR and CNR of the main pulmonary artery, the right pulmonary artery and the left pulmonary artery in the standard kernel DL-H group were significantly higher than the ASiR-V group(SNR: 25.5±7.1 vs 14.5±3.9, 23.9±7.2 vs 13.9±3.4, 24.9±7.4 vs 14.8±4.1, CNR: 21.6±6.6 vs 12.3±3.9, 20.2±6.7 vs 11.8±3.4, 21.2±6.9 vs 12.6±4.1, respectively;all <0.001). The subjective image quality score of the standard kernel DL-H group was significantly higher than the ASiR-V group (4.6 vs 3.8, <0.001). There were no significant difference in the Qanadli embolism index, positive rate and positive Qanadli embolism index between the two groups (all >0.05). Compared with ASiR-V reconstruction algorithms group, standard kernel DL-H reconstruction algorithms can significantly improve the image quality of dual low-dose CTPA.

Authors

  • S M Qiu
    Department of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou 221002, China.
  • H Zhang
    Quantum Science and Engineering Centre (QSec), Nanyang Technological University, Singapore, 639798, Singapore.
  • Z X Liu
    Department of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou 221002, China.
  • L Zhang
    Department of Radiology, Baoji Center Hospital, Baoji, 721008, Shaanxi, China; Department of Radiology, Baoji Hi-Tech People's Hospital, Baoji, 721013, Shaanxi, China.
  • Y K Meng
    Department of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou 221002, China.
  • X N Sun
    Department of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou 221002, China.
  • L X Xie
    Qingdao Eye Hospital, Shandong Eye Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Qingdao 266071, China.
  • Y C Zhang
    Department of General Surgery, the Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi 214023, China.
  • H Wang
    Department of Mechanical Engineering, Columbia University, 500 West 120th Street, New York, NY 10027, USA.
  • K Xu
    Qiushi Academy for Advanced Studies, Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang University, and Department of Biomedical Engineering, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou 310027, China xykd@zju.edu.cn.