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Lung MRI: Indications, Capabilities, and Techniques—AJR Expert Panel Narrative Review

  • Lea Azour
  • , Yoshiharu Ohno
  • , Jürgen Biederer
  • , Bruno Hochhegger
  • , Grzegorz Bauman
  • , Hiroto Hatabu
  • , Mark L. Schiebler
  • , Jeanne B. Ackman

Research output: Contribution to journalReview articlepeer-review

Abstract

Lung MRI provides both structural and functional information across a spectrum of parenchymal and airway pathologies. MRI, using current widely available conventional sequences, provides high-quality diagnostic images that allow tissue characterization and delineation of lung lesions; dynamic evaluation of expiratory central airway collapse, diaphragmatic or chest wall motion, and the relation of lung masses to the chest wall; oncologic staging; surveillance of chronic lung pathologies; and differentiation of inflammation and fibrosis in interstitial lung disease. Ongoing technologic advances, including deep learning acceleration methods, may enable future applications in longitudinal lung cancer screening without ionizing radiation exposure and in the regional quantification of ventilation and perfusion without hyperpolarized gas or IV contrast media. Although society statements highlight appropriate indications for lung MRI and the modality has performed favorably relative to CT or FDG PET/CT in various indications, the examination’s clinical utilization remains extremely low. Ongoing barriers to adoption include limited awareness by referring physicians, as well as insufficient proficiency and experience by radiologists and technologists. In this AJR Expert Panel Narrative Review, we review the clinical indications for lung MRI, describe the examination’s current capabilities, provide guidance on protocols comprising widely available pulse sequences, introduce emerging techniques, and issue consensus recommendations.

Original languageEnglish
Article numbere2532637
JournalAmerican Journal of Roentgenology
Volume225
Issue number4
DOIs
Publication statusPublished - 10-2025
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

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