Semiautomated Ventilation Defect Quantification in Exercise-induced Bronchoconstriction Using Hyperpolarized Helium-3 Magnetic Resonance Imaging: A Repeatability Study.
Journal Article (Journal Article)
RATIONALE AND OBJECTIVES: This study aimed to compare the performance of a semiautomated ventilation defect segmentation approach, adaptive K-means, with manual segmentation of hyperpolarized helium-3 magnetic resonance imaging in subjects with exercise-induced bronchoconstriction (EIB). MATERIALS AND METHODS: Six subjects with EIB underwent hyperpolarized helium-3 magnetic resonance imaging and spirometry tests at baseline, post exercise, and recovery over two separate visits. Ventilation defects were analyzed by two methods. First, two independent readers manually segmented ventilation defects. Second, defects were quantified by an adaptive K-means method that corrected for coil sensitivity, applied a vesselness filter to estimate pulmonary vasculature, and segmented defects adaptively based on the overall low-intensity signals in the lungs. These two methods were then compared in four aspects: (1) ventilation defect percent (VDP) measurements, (2) correlation between spirometric measures and measured VDP, (3) regional VDP variations pre- and post exercise challenge, and (4) Dice coefficient for spatial agreement. RESULTS: The adaptive K-means method was ~5 times faster, and the measured VDP bias was under 2%. The correlation between predicted forced expiratory volume in 1 second over forced vital capacity and VDP measured by adaptive K-means (ρ = -0.64, P <0.0001) and by the manual method (ρ = -0.63, P <0.0001) yielded almost identical 95% confidence intervals. Neither method of measuring VDP indicated apical/basal or anterior dependence in this small study cohort. CONCLUSIONS: Compared to the manual method, the adaptive K-means method provided faster, reproducible, comparable measures of VDP in EIB and may be applied to a variety of lung diseases.
- Zha, W; Niles, DJ; Kruger, SJ; Dardzinski, BJ; Cadman, RV; Mummy, DG; Nagle, SK; Fain, SB
- September 2016
Volume / Issue
- 23 / 9
Start / End Page
- 1104 - 1114
Pubmed Central ID
Electronic International Standard Serial Number (EISSN)
Digital Object Identifier (DOI)
- United States