Deep learning-driven multi-view multi-task image quality assessment method for chest CT image.

Journal: Biomedical engineering online
Published Date: Dec 6, 2023

Abstract

BACKGROUND: Chest computed tomography (CT) image quality impacts radiologists' diagnoses. Pre-diagnostic image quality assessment is essential but labor-intensive and may have human limitations (fatigue, perceptual biases, and cognitive biases). This study aims to develop and validate a deep learning (DL)-driven multi-view multi-task image quality assessment (M[Formula: see text]IQA) method for assessing the quality of chest CT images in patients, to determine if they are suitable for assessing the patient's physical condition.

Authors

  • Jialin Su
    School of Optoelectronic and Communication Engineering, Xiamen University of Technology, Xiamen, 361024, China.
  • Meifang Li
    Department of Medical Imaging, Affiliated Hospital of Putian University, Putian, 351100, China.
  • Yongping Lin
  • Liu Xiong
    School of Optoelectronic and Communication Engineering, Xiamen University of Technology, Xiamen, 361024, China.
  • Caixing Yuan
    Department of Medical Imaging, Affiliated Hospital of Putian University, Putian, 351100, China.
  • Zhimin Zhou
    Department of Medical Imaging, Affiliated Hospital of Putian University, Putian, 351100, China.
  • Kunlong Yan
    Department of Medical Imaging, Affiliated Hospital of Putian University, Putian, 351100, China.

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