Journal of magnetic resonance imaging : JMRI · 2025
3D Automated Segmentation of Bronchial Abnormalities on Ultrashort Echo Time MRI: A Quantitative MR Outcome in Cystic Fibrosis.
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Bouzid Amel Imene Hadj, Benlala Ilyes, de Senneville Baudouin Denis, Baldacci Fabien, Macey Julie, Benhassen Wadie, Su Pan, Bui Stephanie, Zysman Maeva, Bustin Aurelien, Berger Patrick, Dournes Gael
Аннотация
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.
EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: 4.
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.
EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: 4.