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Abstract #4090

MR Estimation of Permeability Parameters in Dynamic Contrast Enhanced Studies Using Model Averaging Technique and Nested Model Selection Method

Hassan Bagher-Ebadian 1,2 , Siamak P. Nejad-Davarani 3,4 , James R Ewing 2,3 , Tom Mikkelsen 5 , Rajan Jain 6 , Lisa Scarpace 5 , and Hamid Soltanian-Zadeh 1,7

1 Radiology, Henry Ford Hospital, Detroit, MI, United States, 2 Physics, Oakland University, Rochester, MI, United States, 3 Neurology, Henry Ford Hospital, Detroit, MI, United States, 4 Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, 5 Neurosurgery, Henry Ford Hospital, Detroit, MI, United States, 6 Radiology, NYU Langone Medical Center, NY, United States, 7 CIPCE, ECE Dept., University of Tehran, Tehran, Iran

A nested model selection (NMS) technique along with physiological concepts of the models is introduced and a model-averaging technique in Dynamic-Contrast-Enhanced (DCE)-MR model selection using the Akaike-Information-Criterion (AIC) is constructed. The Models in NMS are recruited in the AIC and applied to an exemplary DCE-MR data of a patient with Glioblastoma-Multiforme. Model-choice and probability maps estimated from both techniques are compared. The AIC and NMS provide unique set of probability maps for estimating the contribution of each model in a specific voxel. These probabilities allow combining the estimations from different models, thus generating a more accurate estimate of permeability parameters.

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