Evaluating Second-Generation Deep Learning Technique for Noise Reduction in Myocardial T1-Mapping Magnetic Resonance Imaging.

Journal: Diseases (Basel, Switzerland)
Published Date: May 18, 2025

Abstract

BACKGROUND: T1 mapping has become a valuable technique in cardiac magnetic resonance imaging (CMR) for evaluating myocardial tissue properties. However, its quantitative accuracy remains limited by noise-related variability. Super-resolution deep learning-based reconstruction (SR-DLR) has shown potential in enhancing image quality across various MRI applications, yet its effectiveness in myocardial T1 mapping has not been thoroughly investigated. This study aimed to evaluate the impact of SR-DLR on noise reduction and measurement consistency in myocardial T1 mapping.

Authors

  • Shungo Sawamura
    Department of Diagnostic Radiology, Graduate School of Medicine, Yokohama City University, Yokohama 232-0006, Kanagawa, Japan.
  • Shingo Kato
    Diagnostic Radiology, Yokohama City University Graduate School of Medicine, Yokohama-shi, Japan.
  • Naofumi Yasuda
    Department of Radiology, Kanagawa Cardiovascular and Respiratory Center, Kanagawa, Japan.
  • Takumi Iwahashi
    Department of Radiology, Yokosuka Kyosai Hospital, Yokosuka 238-8558, Kanagawa, Japan.
  • Takamasa Hirano
    Department of Radiology, Yokohama City University Hospital, Yokohama 232-0006, Kanagawa, Japan.
  • Taiga Kato
    Department of Radiology, Yokohama City University Hospital, Yokohama 232-0006, Kanagawa, Japan.
  • Daisuke Utsunomiya
    Department of Diagnostic Radiology, Graduate School of Life Sciences, Kumamoto University, 1-1-1 Honjo, Chuoku, Kumamoto, 860-8556, Japan. Electronic address: d.utsunomiya@gmail.com.

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