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.