Maximum-Entropy Density Estimation for MRI Stochastic Surrogate Models
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11. maxEnt.pdf
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Author(s)
Zhang, Zheng
Farnoosh, Niloofar
Klemas, Thomas J.
Daniel, Luca
Date Issued
August 2014
Journal
IEEE Antennas and Wireless Propagation Letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Zhang, Zheng; Farnoosh, Niloofar; Klemas, Thomas and Daniel, Luca. “Maximum-Entropy Density Estimation for MRI Stochastic Surrogate Models.” IEEE Antennas and Wireless Propagation Letters 13 (2014): 1656–1659.© 2014 Institute of Electrical and Electronics Engineers (IEEE)
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Author's final manuscript
Abstract
Stochastic spectral methods can generate accurate compact stochastic models for electromagnetic problems with material and geometric uncertainties. This letter presents an improved implementation of the maximum-entropy algorithm to compute the density function of an obtained generalized polynomial chaos expansion in magnetic resonance imaging (MRI) applications. Instead of using statistical moments, we show that the expectations of some orthonormal polynomials can be better constraints for the optimization flow. The proposed algorithm is coupled with a finite element-boundary element method (FEM-BEM) domain decomposition field solver to obtain a robust computational prototyping for MRI problems with low- and high-dimensional uncertainties.
MIT Department
Lincoln Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Research Laboratory of Electronics
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DOI of Published Version
https://doi.org/10.1109/lawp.2014.2349933
