Quantitative Magnetic Resonance Imaging of Bronchopulmonary Dysplasia in the Neonatal Intensive Care Unit Environment.

RATIONALE: Bronchopulmonary dysplasia (BPD) is a prevalent yet poorly characterized pulmonary complication of premature birth; the current definition is based solely on oxygen dependence at 36 weeks postmenstrual age without objective measurements of structural abnormalities across disease severity. OBJECTIVES: We hypothesize that magnetic resonance imaging (MRI) can spatially resolve and quantify the structural abnormalities of the neonatal lung parenchyma associated with premature birth. METHODS: Using a unique, small-footprint, 1.5-T MRI scanner within our neonatal intensive care unit (NICU), diagnostic-quality MRIs using commercially available sequences (gradient echo and spin echo) were acquired during quiet breathing in six patients with BPD, six premature patients without diagnosed BPD, and six full-term NICU patients (gestational ages, 23-39 wk) at near term-equivalent age, without administration of sedation or intravenous contrast. Images were scored by a radiologist using a modified Ochiai score, and volumes of high- and low-signal intensity lung parenchyma were quantified by segmentation and threshold analysis. MEASUREMENTS AND MAIN RESULTS: Signal increases, putatively combinations of fibrosis, edema, and atelectasis, were present in all premature infants. Infants with diagnosed BPD had significantly greater volume of high-signal lung (mean ± SD, 26.1 ± 13.8%) compared with full-term infants (7.3 ± 8.2%; P = 0.020) and premature infants without BPD (8.2 ± 6.4%; P = 0.026). Signal decreases, presumably alveolar simplification, only appeared in the most severe BPD cases, although cystic appearance did increase with severity. CONCLUSIONS: Pulmonary MRI reveals quantifiable, significant differences between patients with BPD, premature patients without BPD, and full-term control subjects. These methods could be implemented to individually phenotype disease, which may impact clinical care and predict future outcomes.