Super-resolution deep learning in pediatric CTA for congenital heart disease: enhancing intracardiac visualization under free-breathing conditions.

Journal: European radiology
Published Date: Jul 16, 2025

Abstract

OBJECTIVE: This study assesses the effectiveness of super-resolution deep learning reconstruction (SR-DLR), conventional deep learning reconstruction (C-DLR), and hybrid iterative reconstruction (HIR) in enhancing image quality and diagnostic performance for pediatric congenital heart disease (CHD) in CT angiography (CCTA).

Authors

  • Xinyan Zhou
    Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
  • Daoqiang Xiong
    Department of Radiology, Kunming Yan'an Hospital (Yan'an Hospital Affiliated to Kunming Medical University), Kunming, China.
  • Fei Liu
    Department of Interventional Radiology, Qinghai Red Cross Hospital, Xining, Qinghai, China.
  • Junyi Li
    School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China. Electronic address: lijunyi@hit.edu.cn.
  • Na Tan
    Department of Radiology, Kunming Yan'an Hospital (Yan'an Hospital Affiliated to Kunming Medical University), Kunming, China.
  • Xirui Duan
    Department of Radiology, Kunming Yan'an Hospital (Yan'an Hospital Affiliated to Kunming Medical University), Kunming, China.
  • Xiaolan Du
    Department of Radiology, Kunming Yan'an Hospital (Yan'an Hospital Affiliated to Kunming Medical University), Kunming, China.
  • Zhiqiang Ouyang
    Department of Radiology, Kunming Yan'an Hospital (Yan'an Hospital Affiliated to Kunming Medical University), Kunming, China.
  • Shasha Bao
    Department of Radiology, Kunming Yan'an Hospital (Yan'an Hospital Affiliated to Kunming Medical University), Kunming, China.
  • Tengfei Ke
    Department of Radiology, Yunnan Cancer Hospital (The Third Affiliated Hospital of Kunming Medical University), Kunming, China.
  • Yihan Zhao
    College of Veterinary Medicine, China Agricultural University, Beijing, China.
  • Jianxiang Tao
    Department of Radiology, Kunming Yan'an Hospital (Yan'an Hospital Affiliated to Kunming Medical University), Kunming, China.
  • Xuemin Dong
    Department of Radiology, Kunming Yan'an Hospital (Yan'an Hospital Affiliated to Kunming Medical University), Kunming, China.
  • Yining Wang
    Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
  • Chengde Liao
    Department of Radiology, Kunming Yan'an Hospital (Yan'an Hospital Affiliated to Kunming Medical University), Kunming, China. chengdeliao@qq.com.

Keywords

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