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Proceedings Paper

Bayesian estimation for rheological MRI
Author(s): Fabien Feron; Ken D. Sauer
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Paper Abstract

Magnetic resonance imaging (MRI) is used, in addition to its well known medical and biological applications, for the study of a variety of fluid dynamic phenomena. This paper focuses on the MRI imaging of liquid foams to aid the study of their temporal and spatial dynamics. The three dimensional image reconstruction problem is relatively low SNR, with the ultimate goal of analyzing the foam's structure and its evolution. We demonstrate substantial improvement of image quality with Bayesian estimation using simple edge preserving Markov random field (MRF) models of the fluid field. In terms of total computation time, speed of convergence of estimates is similar between gradient based methods and sequential greedy voxel updates, with the former requiring more iterations and the latter requiring more operations per iteration. The paper shows also some preliminary results in the analysis of the reconstructed imagery using a simple parametric model of foam cells.

Paper Details

Date Published: 1 July 2003
PDF: 8 pages
Proc. SPIE 5016, Computational Imaging, (1 July 2003); doi: 10.1117/12.479703
Show Author Affiliations
Fabien Feron, Univ. of Notre Dame (United States)
Ken D. Sauer, Univ. of Notre Dame (United States)

Published in SPIE Proceedings Vol. 5016:
Computational Imaging
Charles A. Bouman; Robert L. Stevenson, Editor(s)

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