Lung MRI in Parenchymal Disease

Gaël Dournes, MD, PhD, Centre de Recherche Cardio-thoracique de Bordeaux, Université Bordeaux Segalen, Bordaux, France
Wadie Ben Hassen, Siemens Healthineers, Mérignac, France
 |  03.06.2019

Lung MRI is a powerful tool to assess and follow-up lung diseases without the use of ionizing radiation. However, it has long been considered difficult due to very low lung proton content, susceptibility artifacts at alveolar and parenchymal interfaces, and cardio-respiratory motion. Dr. Dournes (Bordeaux University, France) summarizes recent advances in lung MRI and shows robust evaluation of both morphological and functional information from his clinical experience, focussing on the potential benefit of lung MRI for routine clinical use.

Lung MRI has long been considered beyond the scope of MR examinations due to very low lung proton content, susceptibility artifacts at alveolar and parenchymal interfaces, and cardio-respiratory motion. However, recent technological solutions have emerged that could improve the clinical application of lung MRI. MRI is a radiation-free imaging modality that offers the possibility of combining both morphological and functional information, including tissue contrast characterization.

This is important in an era where novel therapies have revolutionized the management of patients, which can lead to an increased need for repeat imaging to assess response to a certain treatment. In addition, artificial intelligence could allow advanced combination of data to better phenotype patients and/or predict disease outcome, since it goes beyond just morphological information. Thus, lung MRI may eventually prove a powerful tool to determine the full complexity of lung diseases that are still by no means well understood.

Morphological MRI
CT (A, C, E) and 3D UTE spiral VIBE sequence1 (B, D, F) acquired in a patient with cystic fibrosis (A, B); chronic obstructive pulmonary disease (C, D); and idiopathic fibrosis (E, F). Images A and B show indications of proximal airway alteration, such as bronchiectasis, wall thickening, and mucus plugs. Images C and D show destruction of the lung parenchyma. This can be seen as hypoattenuating areas using CT imaging (C) or hyposignal intensity on MRI (D). Images E and F show interstitial modifications such as honeycombing, reticulation, and traction bronchiectasis. Note the good visual agreement between CT and 3D UTE MRI to depict structural alterations at high resolution.

Contrast MRI
T2 BLADE (A), T1 VIBE (B) and Diffusion (C) MR sequences in a female with cystic fibrosis and respiratory exacerbation. There is an area of intra-parenchymal air within a lung consolidation of the left lower lobe (black arrow). The diffusion coefficient map in C confirms a restricted area within a water-filled consolidation. This is compatible with an abscess that requires intensive antibiotic therapy.

Functional MRI
3D UTE (A) and Fourier transform reconstruction of lung perfusion (B) and ventilation (C) in a young female with cystic fibrosis and respiratory exacerbation in a coronal view. Black arrows show areas of reduced perfusion on image B, with a regional distribution resembling the areas of hypointensities on image A. However, there are multiple ventilation defects on image C that do not perfectly match the perfusion defects, reflecting the two different types of information.

About the Author

Dr. Gaël Dournes, M.D., Ph.D., Université de Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, France. INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, France. CHU de Bordeaux, Service d’Imagerie Thoracique et Cardiovasculaire, Service des Maladies Respiratoires, Service d’Exploration Fonctionnelle Respiratoire, Pessac, France.

Share this page:

Verwandte Artikel

1WIP, the product is currently under development and is not for sale in the US and in other countries. Its future availability cannot be ensured.

The statements by Siemens Healthineers customers described herein are based on results that were achieved in the customer’s unique setting. Since there is no “typical” hospital and many variables exist (e.g., hospital size, case mix, level of IT adoption) there can be no guarantee that other customers will achieve the same results.