title: Parameter estimation using macroscopic diffusion MRI signal models.
abstract: Macroscopic models of the diffusion MRI (dMRI) signal can be helpful to understanding the relationship between the tissue microstructure and the dMRI signal. We study the least squares problem associated with estimating tissue parameters such as the cellular volume fraction, the residence times and the effective diffusion coefficients using a recently developed macroscopic model of the dMRI signal called the Finite Pulse Kärger model that generalizes the original Kärger model to non-narrow gradient pulses. In order to analyze the quality of the estimation in a controlled way, we generated synthetic noisy dMRI signals by including the effect of noise on the exact signal produced by the Finite Pulse Kärger model. The noisy signals were then fitted using the macroscopic model. Minimizing the least squares, we estimated the model parameters. The bias and standard deviations of the estimated model parameters as a function of the signal to noise ratio (SNR) were obtained. We discuss the choice of the b-values, the least square weights, the extension to experimentally obtained dMRI data as well noise correction.
authors.affiliation: NeuroSpin, CEA Saclay Center, 91191 Gif-sur-Yvette Cedex, France.
authors.forename: Hang Tuan
authors.initials: HT
authors.lastname: Nguyen
authors.forename: Denis
authors.initials: D
authors.lastname: Grebenkov
authors.forename: Dang
authors.initials: D
authors.lastname: Van Nguyen
authors.forename: Cyril
authors.initials: C
authors.lastname: Poupon
authors.forename: Denis
authors.initials: D
authors.lastname: Le Bihan
authors.forename: Jing-Rebecca
authors.initials: JR
authors.lastname: Li
mesh_terms: D000465:Algorithms; D000818:Animals; D002540:Cerebral Cortex / Q000166:cytology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D051381:Rats; D012044:Regression Analysis; D015203:Reproducibility of Results; D059629:Signal-To-Noise Ratio