Analysis of Upper Airway Morphology Using Four-Dimensional Dynamic MRI With Active Deep Learning-Based Automatic Segmentation.

backgroundUpper-airway morphology changes during breathing can be captured with cine 4D MRI. Active-learning nnU-Net reduces manual labeling while maintaining accuracy. PURPOSE: For automatic upper airway segmentation on free-breathing cine 4D MRI using active learning and quantifying dynamic changes under two mouth positions. STUDY TYPE: Prospective cross-sectional study. POPULATION: Eighty-four OSA (obstructive sleep apnea)-free adults (28 M/56F; 18-80 years; 33 with sleep-related breathing symptoms). Segmentation performance was evaluated on an internal test set (n = 18). FIELDSTRENGTH/SEQUENCE: 3T, free-breathing time-resolved imaging with interleaved stochastic trajectories (TWIST) sequence under closed- and open-mouth positions. ASSESSMENT: Manual annotations by a technologist (radiologist-verified) served as reference standard and training labels for an active-learning nnU-Net (68 training; four fixed validation). Total airway length, cross-sectional area (CSA), and total airway volume were computed at each anatomical level and compared across mouth positions, sex, and sleep-related symptom status, and independent predictors were identified. STATISTICAL TESTS: Paired/unpaired t or Mann-Whitney U test (two-sided p = 0.05). Predictor selection by 10-fold LASSO; effects estimated via ordinary least squares with cluster-robust standard errors.

resultsSegmentation achieved a dice 0.959 ± 0.019 (test set). Open-mouth breathing significantly lengthened the total airway (7.92 ± 1.07 vs. 7.41 ± 0.93 cm) and reduced retropalatal CSA (1.51 ± 0.68 vs. 1.80 ± 0.69 cm DATA

conclusionFour-dimensional cine MRI with active-learning nnU-Net can automatically quantify dynamic upper airway morphology, demonstrating systematic differences and dynamic variability. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 2.