Ventilation defect quantification on 3He MRI through deep learning: the MESA COPD Study

Abstract

Purpose: Hyperpolarized helium (3He) MRI is used to assess ventilation defects in COPD and other pulmonary diseases. Existing segmentation methods are very operator-dependent and effort consuming. We aimed to develop a deep-learning based method to accelerate segmentation, eliminate operator dependence and improve reliability. Methods and Materials: The MESA COPD Study is a nested case-control study of COPD (post-bronchodilator FEV1/FVC ratio Results: The percentage of non-, low-, and normal- ventilated regions were 12±9%, 25±14%, and 62±20%, respectively. At patient level, the dice coefficient scores were not significantly different between participants with and without COPD (p=0.29-0.95, Table). Conclusion: The proposed deep learning method yields accurate, automated segmentation of ventilation defects among older smokers, independent of COPD status. Funding NIH/NHLBI R01-HL093081, R01-HL077612, R01-HL121270