Keywords: Hyperpolarized MR (Gas), Hyperpolarized MR (Gas), Machine Learning, Hyperpolarized Xenon MRI, Chronic Obstructive Pulmonary Disease, Interstitial Lung Disease
Motivation: 129Xe MRI/MRS can assess distinct aspects of pulmonary gas exchange and hemodynamics. However, there is no gold standard against which these metrics can be validated.
Goal(s): To evaluate whether unsupervised cluster analysis of 129Xe MRI/MRS metrics naturally reveal patterns known to be associated with certain disease groups.
Approach: Eight 129Xe MRI/MRS features were subjected to k-means clustering with internal validation indices used to determine optimal cluster number.
Results: The analysis identified four clusters that largely distinguished healthy, COPD, and ILD patient groups.
Impact: This study offers a pathway for designing future prospective clinical trials that could validate non-invasive 129Xe MRI/MRS metrics of gas exchange by demonstrating that certain patterns distinguish between lung disease subtypes with high accuracy.
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