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

Abstract

Background: Upper-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.

Results: Segmentation 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 cm²). Coefficients of variation (CVs) for CSA and volume were significantly higher with 20-s open-mouth breathing. Males (n = 28) exhibited significantly larger airway volumes than females (closed 27.94 ± 4.87 vs. 19.82 ± 3.26 cm³; open 30.26 ± 5.94 vs. 20.94 ± 3.85 cm³). Symptomatic individuals (n = 33) had significantly longer airways (closed 7.96 ± 0.96 vs. 7.04 ± 0.70 cm; open 8.54 ± 1.01 vs. 7.52 ± 0.91 cm), narrower open-mouth retropalatal CSA (1.24 ± 0.51 vs. 1.68 ± 0.72 cm²), and greater retropalatal CSA dynamic variability. Multivariable regression confirmed mouth position, symptoms, and sex as independent predictors.

Data conclusion: Four-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.