Meeting Banner
Abstract #0755

Are Two Samples of Parametric Images Statistically Different? Novel Significane Tests on Samples of Density Estimates, with Application to Ventilation-To-Perfusion Mapping of the Lung in COPD Using Oxygen-Enhanced MRI

Chris J. Rose1, 2, Penny L. Hubbard1, 2, Tim F. Cootes1, 2, Chris J. Taylor1, 2, Josephine H. Naish1, 2, Geoff J. Parker1, 2, Simon S. Young3, John C. Waterton, 14

1University of Manchester Biomedical Imaging Institute, Manchester, Greater Manchester, United Kingdom; 2University of Manchester Academic Health Science Centre, Manchester, Greater Manchester, United Kingdom; 3AstraZeneca R&D, Charnwood, Loughborough, Leicestershire, United Kingdom; 4AstraZeneca, Alderley Park, Macclesfield, Cheshire, United Kingdom

MRI is used in natural history and intervention studies to spatially map parameters throughout organs or tumors. However, the problem of choosing the best method for drawing inferences about the populations being studied is unsolved. Conventionally, a significance test is applied using averaged parameter values, but this method neglects heterogeneity. We developed significance tests for application to a sample of density estimates (smooth histograms). Using ventilation-to-perfusion (V/Q) ratio data from an oxygen-enhanced MRI study of COPD patients and age-matched controls, we can identify biologically meaningful significant differences in V/Q where the conventional method fails.