Magnetic Resonance Imaging in the Clinical Evaluation of Lung Disorders: Current Status and Future Prospects.

Journal: Journal of magnetic resonance imaging : JMRI
Published Date: May 9, 2025

Abstract

The low proton density and high signal decay rate of pulmonary tissue have previously hampered the application of magnetic resonance imaging (MRI) in the clinical evaluation of lung disorders. With the continuing technical advances in scanners, coils, pulse sequences, and image postprocessing, pulmonary MRI can provide structural and functional information with faster imaging speed and improved image quality, which has shown potential to be an alternative and complementary diagnostic method to chest computed tomography (CT). Compared with CT, MRI does not involve ionizing radiation, making it particularly suitable for pediatric patients, pregnant women, and individuals requiring longitudinal monitoring. This narrative review focuses on recent advances in techniques and clinical applications for pulmonary MRI in lung diseases, including lung parenchymal and pulmonary vascular diseases. Future developments, including artificial intelligence-driven technological optimization and assisted diagnosis, hardware advancements, and clinical biomarkers validation, hold the potential to further enhance the clinical utility of pulmonary MRI. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.

Authors

  • Linyu Wu
    Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China.
  • Chen Gao
    Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China.
  • Ting Wu
    Asia Pacific Unit, Department of Pharmacoepidemiology, MSD (China) R&D Co., Ltd., Beijing, China.
  • Ning Kong
    Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
  • Ziwei Zhang
    College of Chemistry, Jilin University, Qianjin Street 2699, Changchun, Jilin, 130012, China. zzw@jlu.edu.cn.
  • Jie Li
    Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence Application Technology Research Institute, Shenzhen Polytechnic University, Shenzhen, China.
  • Li Fan
    Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
  • Maosheng Xu
    Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China.

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