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The purpose of this study is to investigate a method, using simulations, toimprove contrast agent quantification in Dynamic Contrast Enhanced MRI.Bayesian hierarchical models (BHMs) are applied to smaller images($10\times10\times10$) such that spatial information can be incorporated. Thenexploratory analysis is done for larger images ($64\times64\times64$) by usingmaximum a posteriori (MAP).  For smaller images: the estimators of proposed BHMs show improvements interms of the root mean squared error compared to the estimators in existingmethod for a noise level equivalent of a 12-channel head coil at 3T. Moreover,Leroux model outperforms Besag models. For larger images: MAP estimators alsoshow improvements by assigning Leroux prior.

Contrast Agent Quantification by Using Spatial Information in Dynamic Contrast Enhanced MRI