An efficient dual-domain deep learning network for sparse-view CT reconstruction.

Journal: Computer methods and programs in biomedicine
Published Date: Aug 16, 2024

Abstract

BACKGROUND AND OBJECTIVE: We develop an efficient deep-learning based dual-domain reconstruction method for sparse-view CT reconstruction with small training parameters and comparable running time. We aim to investigate the model's capability and its clinical value by performing objective and subjective quality assessments using clinical CT projection data acquired on commercial scanners.

Authors

  • Chang Sun
    Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China.
  • Yazdan Salimi
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.
  • Neroladaki Angeliki
    Geneva University Hospital, Division of Radiology, CH-1211, Geneva, Switzerland.
  • Sana Boudabbous
    Geneva University Hospital, Division of Radiology, CH-1211, Geneva, Switzerland.
  • Habib Zaidi
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland. habib.zaidi@hcuge.ch.

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