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10.18383/j.tom.2015.00133

http://scihub22266oqcxt.onion/10.18383/j.tom.2015.00133
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C6024409!6024409!30042955
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suck abstract from ncbi


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pmid30042955      Tomography 2015 ; 1 (1): 61-8
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  • Renal DCE-MRI Model Selection Using Bayesian Probability Theory #MMPMID30042955
  • Beeman SC; Osei-Owusu P; Duan C; Engelbach J; Bretthorst GL; Ackerman JJH; Blumer KJ; Garbow JR
  • Tomography 2015[Sep]; 1 (1): 61-8 PMID30042955show ga
  • The goal of this work was to demonstrate the utility of Bayesian probability theory-based model selection for choosing the optimal mathematical model from among 4 competing models of renal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. DCE-MRI data were collected on 21 mice with high (n = 7), low (n = 7), or normal (n = 7) renal blood flow (RBF). Model parameters and posterior probabilities of 4 renal DCE-MRI models were estimated using Bayesian-based methods. Models investigated included (1) an empirical model that contained a monoexponential decay (washout) term and a constant offset, (2) an empirical model with a biexponential decay term (empirical/biexponential model), (3) the Patlak?Rutland model, and (4) the 2-compartment kidney model. Joint Bayesian model selection/parameter estimation demonstrated that the empirical/biexponential model was strongly favored for all 3 cohorts, the modeled DCE signals that characterized each of the 3 cohorts were distinctly different, and individual empirical/biexponential model parameter values clearly distinguished cohorts of low and high RBF from one another. The Bayesian methods can be readily extended to a variety of model analyses, making it a versatile and valuable tool for model selection and parameter estimation.
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