Keywords: AI Diffusion Models, Hyperpolarized MR (Gas)
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.
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