Quantitative lung ultrasound detects dynamic changes in lung recruitment in the preterm lamb

BACKGROUND: Lung ultrasound (LUS) may not detect small, dynamic changes in lung volume. Mean greyscale measurement using computer-assisted image analysis (Q-LUS(MGV)) may improve the precision of these measurements. METHODS: Preterm lambs (n = 40) underwent LUS of the dependent or non-dependent lung during static pressure–volume curve mapping. Total and regional lung volumes were determined using the super-syringe technique and electrical impedance tomography. Q-LUS(MGV) and gold standard measurements of lung volume were compared in 520 images. RESULTS: Dependent Q-LUS(MGV) moderately correlated with total lung volume (rho = 0.60, 95% CI 0.51–0.67) and fairly with right whole (rho = 0.39, 0.27–0.49), central (rho = 0.38, 0.27–0.48), ventral (rho = 0.41, 0.31–0.51) and dorsal regional lung volumes (rho = 0.32, 0.21–0.43). Non-dependent Q-LUS(MGV) moderately correlated with total lung volume (rho = 0.57, 0.48–0.65) and fairly with right whole (rho = 0.43, 0.32–0.52), central (rho = 0.46, 0.35–0.55), ventral (rho = 0.36, 0.25–0.47) and dorsal lung volumes (rho = 0.36, 0.25–0.47). All correlation coefficients were statistically significant. Distinct inflation and deflation limbs, and sonographic pulmonary hysteresis occurred in 95% of lambs. The greatest changes in Q-LUS(MGV) occurred at the opening and closing pressures. CONCLUSION: Q-LUS(MGV) detected changes in total and regional lung volume and offers objective quantification of LUS images, and may improve bedside discrimination of real-time changes in lung volume. IMPACT: Lung ultrasound (LUS) offers continuous, radiation-free imaging that may play a role in assessing lung recruitment but may not detect small changes in lung volume. Mean greyscale image analysis using computer-assisted quantitative LUS (Q-LUS(MGV)) moderately correlated with changes in total and regional lung volume. Q-LUS(MGV) identified opening and closing pressure and pulmonary hysteresis in 95% of lambs. Computer-assisted image analysis may enhance LUS estimation of lung recruitment at the bedside. Future research should focus on improving precision prior to clinical translation.