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Modelling of cardiac imaging data with spatial correlation.

Abstract

Cardiac imaging with single photon emission computed tomography (SPECT) is a common approach for quantifying myocardial perfusion. Fusing serial SPECT studies allows detection and quantification of changes in myocardial perfusion such as those resulting from disease progression or successful treatment therapy for patients with coronary artery disease. The abundance of data for each subject along with the inherent intra-subject correlation due to spatial proximity of multiple perfusion measurements present special analytical challenges. We utilize a standard physiological model of the left ventricle (LV) to construct a general statistical model for cardiac perfusion that incorporates spatial correlation. We illustrate the use of mixed effects models and linear models with correlated errors to estimate myocardial perfusion counts and to compare these counts across serial studies. We address different types of spatial correlation among perfusion measurements in the LV, and we consider various parametric structures for these correlations. We apply the model to data from serial SPECT studies conducted while subjects were in both restful and stressful states approximately two days and one year following a myocardial infarction.

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