Previous 129Xe-MRI binning analysis techniques assumed normal distributions or relied on a Box-Cox transformation. Adapting these binning methods to account for known covariates of lung function and structure, such as age and height, was not straightforward. To account for these limitations, a new binning method is proposed here that uses the same modeling techniques used to increase sensitivity and specificity in spirometry and diffusing capacity for carbon monoxide (DLCO). By accounting for MRI image distribution shapes and covariates, healthy representative data distributions are better modelled and can permit increased sensitivity and specificity, particularly for early cardiopulmonary disease progression.
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