Bayesian estimation for rheological MRI

Fabien Feron; Ken D. Sauer

doi:10.1117/12.479703

1 July 2003 Bayesian estimation for rheological MRI

Fabien Feron, Ken D. Sauer

Proceedings Volume 5016, Computational Imaging; (2003) https://doi.org/10.1117/12.479703
Event: Electronic Imaging 2003, 2003, Santa Clara, CA, United States

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.

Citation Download Citation

Fabien Feron and Ken D. Sauer "Bayesian estimation for rheological MRI", Proc. SPIE 5016, Computational Imaging, (1 July 2003); https://doi.org/10.1117/12.479703

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