title: Bayesian analysis of in vivo dynamic 13C-edited 1H images.
abstract: We propose an application of a Bayesian methodology to dynamic MR images of protons J-coupled to 13C nuclei for monitoring the in vivo 13C-glucose uptake of mouse brain. The very low population of these protons and the random noise make the analysis of these images extremely difficult. The proposed method restores the images and provides an "activation" map of the mouse brain by means of a hypothesis testing procedure. The restoration step is performed in the Bayesian framework so that among the other advantages of a stochastic approach, it is possible to model spatial and temporal information about neighboring pixels. This leads to a restoration procedure able to reduce the noise level while preserving the information about the edges of signal areas. Based on the restored images, the testing procedure provides us with a reliable map of pixels characterized by the 13C-glucose uptake.
authors.affiliation: Istituto per le Applicazioni del Calcolo, Consiglio Nazionale delle Richeeche, I-00161 Rome, Italy.
authors.forename: Francesco
authors.initials: F
authors.lastname: de Pasquale
authors.forename: Claudia
authors.initials: C
authors.lastname: Testa
authors.forename: Raphelle
authors.initials: R
authors.lastname: Soldaini
authors.forename: Cinzia
authors.initials: C
authors.lastname: Casieri
authors.forename: Franca
authors.initials: F
authors.lastname: Podo
authors.forename: Francesco
authors.initials: F
authors.lastname: De Luca
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D001931:Brain Mapping / Q000379:methods*; D002247:Carbon Isotopes; D005947:Glucose / Q000378:metabolism*; D007091:Image Processing, Computer-Assisted; D051379:Mice; D009010:Monte Carlo Method; D011522:Protons
chemical_list: D002247:Carbon Isotopes; D011522:Protons; D005947:Glucose