Alveolar Airspace Size in Healthy and Diseased Infant Lungs Measured via Hyperpolarized 3He Gas Diffusion Magnetic Resonance Imaging
- 11 November 2020
- journal article
- research article
- Published by S. Karger AG in Neonatology
- Vol. 117 (6), 704-712
- https://doi.org/10.1159/000511084
Abstract
Background: Alveolar development and lung parenchymal simplification are not well characterized in vivo in neonatal patients with respiratory morbidities, such as bronchopulmonary dysplasia (BPD). Hyperpolarized (HP) gas diffusion magnetic resonance imaging (MRI) is a sensitive, safe, nonionizing, and noninvasive biomarker for measuring airspace size in vivo but has not yet been implemented in young infants. Objective: This work quantified alveolar airspace size via HP gas diffusion MRI in healthy and diseased explanted infant lung specimens, with comparison to histological morphometry. Methods: Lung specimens from 8 infants were obtained: 7 healthy left upper lobes (0–16 months, post-autopsy) and 1 left lung with filamin-A mutation, closely representing BPD lung disease (11 months, post-transplantation). Specimens were imaged using HP 3He diffusion MRI to generate apparent diffusion coefficients (ADCs) as biomarkers of alveolar airspace size, with comparison to mean linear intercept (Lm) via quantitative histology. Results: Mean ADC and Lm were significantly increased throughout the diseased specimen (ADC = 0.26 ± 0.06 cm2/s, Lm = 587 ± 212 µm) compared with healthy specimens (ADC = 0.14 ± 0.03 cm2/s, Lm = 133 ± 37 µm; p < 1 × 10−7); increased values reflect enlarged airspaces. Mean ADCs in healthy specimens were significantly correlated to Lm (r = 0.69, p = 0.041). Conclusions: HP gas diffusion MRI is sensitive to healthy and diseased regional alveolar airspace size in infant lungs, with good comparison to quantitative histology in ex vivo specimens. This work demonstrates the translational potential of gas MRI techniques for in vivo assessment of normal and abnormal alveolar development in neonates with pulmonary disease.Keywords
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