Fully automated image quality assessment based on deep learning for carotid computed tomography angiography: A multicenter study.

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Published Date: Jun 1, 2025

Abstract

PURPOSE: To develop and evaluate the performance of fully automated model based on deep learning and multiple logistics regression algorithm for image quality assessment (IQA) of carotid computed tomography angiography (CTA) images.

Authors

  • Wanyun Fu
    Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou 310014, Zhejiang, China.; The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou 310053 Zhejiang, China.
  • Zhangman Ma
    The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou 310053 Zhejiang, China.
  • Zhiwen Yang
    ShuKun Technology Co., Ltd., Jinhui Bd, Qiyang Rd, Beijing 100029, China.
  • Shufeng Yu
    Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou 310014, Zhejiang, China.
  • Yongsheng Zhang
    Department of Radiology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, China.
  • Xinsheng Zhang
    The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou 310053 Zhejiang, China.
  • Bozhe Mei
    Jinzhou Medical University, Jinzhou, Liaoning Province, China.
  • Yu Meng
    Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou 310014, Zhejiang, China.
  • Chune Ma
    ShuKun Technology Co., Ltd., Jinhui Bd, Qiyang Rd, Beijing 100029, China.
  • Xiangyang Gong
    Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou 310014, Zhejiang, China.; Institute of Artificial Intelligence and Remote Imaging, Hangzhou Medical College, Hangzhou 310014, China. Electronic address: gong.xy@vip.163.com.

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