Regularized estimation of flow patterns in MR velocimetry

Author

Herment, A. ; Giovannelli, Jean-Francois ; Mousseaux, E. ; Idier, J. ; Decesare, A. ; Jolivet, O. ; Bittoun, J.

Author_Institution

Inst. Nat. de la Sante et de la Recherche Med., Paris, France

Volume

3

fYear

1996

fDate

16-19 Sep 1996

Firstpage

291

Abstract

A Bayesian estimator of the magnetic resonance (MR) velocity image is proposed. It is based on a Markov model accounting for the spatial structure of the flow velocity. On the other hand, low MR signal intensity yields high uncertainty on the velocity. Such an important property is taken into account through the observation model. The resulting posterior likelihood is optimized using an iterative coordinate descent (ICD) algorithm. Compared to the usual least squares solution, simulation results on flows with parabolic and flat profiles demonstrate a significant gain of in the mean square error

Keywords

Bayes methods; Markov processes; biomedical NMR; blood flow measurement; iterative methods; medical image processing; minimisation; Bayesian estimator; MR velocimetry; Markov model; flat profiles; flow patterns; flow velocity; flowing blood; iterative coordinate descent algorithm; least squares solution; low MR signal intensity; magnetic resonance velocity image; mean square error; observation model; parabolic profiles; posterior likelihood; regularized estimation; spatial structure; Additive noise; Bayesian methods; Blood; Encoding; Frequency; Gaussian noise; Least squares methods; Magnetic resonance; Uncertainty; Uninterruptible power systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 1996. Proceedings., International Conference on

Conference_Location

Lausanne

Print_ISBN

0-7803-3259-8

Type

conf

DOI

10.1109/ICIP.1996.560487

Filename

560487