DocumentCode :
2613036
Title :
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
Link To Document :
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