3D Automated Segmentation of Bronchial Abnormalities on Ultrashort Echo Time MRI : A Quantitative MR Outcome in Cystic Fibrosis

Background Cystic fibrosis (CF) monitoring relies on computed tomography (CT), but ultra‐short echo time MRI (UTE‐MRI) offers a radiation‐free alternative. However, its clinical adoption is hindered by the laborious and subjective manual analysis, which prevents standardized quantification of bronchial abnormalities. Purpose To develop a deep learning (DL) system for the segmentation of CF bronchial abnormalities on UTE‐MRI and assess clinical relevance in patients undergoing cystic fibrosis transmembrane conductance regulator (CFTR) modulator treatment. Study Type Retrospective. Population One‐hundred and sixty‐six CF patients were included (age = 23 ± 11, 48% male), comprising a training set ( n = 97), a test set ( n = 25), and an independent clinical validation cohort ( n = 44). Field Strength/Sequence 1.5T/UTE‐MRI 3D gradient‐echo Spiral Volume Interpolated Breath‐hold Examination (VIBE) sequence. Assessment The RiSeNet architecture was trained using paired UTE‐MRI and CT scans. Its technical performance was evaluated against expert‐refined segmentations and compared to state‐of‐the‐art segmentation models using topology‐aware metrics: Normalized Surface Dice (NSD) and CenterLine Dice (clDice). Clinical validation was performed by correlating automated measurements at baseline (M0) and 1‐year post‐CFTR modulator treatment (M12) with Bhalla scores and pulmonary function tests (FEV1%p). Statistical Tests Student's t ‐test, Mann–Whitney, Wilcoxon, and Chi‐square tests were used for group comparisons. The Spearman test was used to assess correlations. A p value < 0.05 was considered statistically significant. Results In the test group, RiSeNet achieved significantly superior performance versus state‐of‐the‐art with NSD scores of 0.84 for bronchiectasis, 0.90 for wall thickening, and 0.75 for mucus; and clDice scores of 0.69, 0.61, and 0.64, respectively. In the clinical validation group, significant correlations with Bhalla ( ρ = −0.92/−0.85) and FEV1%p ( ρ = −0.68/−0.67) were observed pre/post‐CFTR modulator. Post‐CFTR modulator, FEV1%p improved (69%–92%) with significant reductions in bronchiectasis (3.88–1.25), wall thickening (30.43–3.05), and mucus (53.30–11.80). Data Conclusion RiSeNet may enable semantic segmentation of CF abnormalities on radiation‐free UTE‐MRI.