Conditional Diffusion Probabilistic Model for Quantitative Analysis of Hyperpolarized 129Xe Ventilation Imaging

Motivation: HP 129Xe MRI is remarkably beneficial for investigating structural and functional abnormalities in COPD. Typical VDP calculation methods are based on semi-automatic segmentation to quantify ventilation images, such as k-means. They are highly influenced by image noise and artificial thresholds. Goal(s): Our goal was to improve the accuracy of automatic segmentation-based VDP on different signal-to-noise ratio images with a small amount of training dataset. Approach: We proposed a conditional diffusion probabilistic model for thoracic cavity mask and ventilation mask segmentation. Results: This model can preferably segment the target mask, calculate the VDP, and maintain high robustness compared to other methods. Impact: Our proposed conditional diffusion probabilistic model can preferably automatically segment the thoracic cavity mask and ventilation mask. It can calculate a more accurate VDP, which allows physicians to better evaluate 129Xe ventilation images.