Maximum-Entropy Density Estimation for MRI Stochastic Surrogate Models

被引：3

作者：

Zhang, Zheng ^{[1
]}

Farnoosh, Niloofar ^{[1
]}

Klemas, Thomas ^{[2
]}

Daniel, Luca ^{[1
]}

机构：

[1] MIT, Elect Res Lab, Cambridge, MA 02139 USA

[2] MIT, Lincoln Lab, Cambridge, MA 02421 USA

来源：

IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS | 2014年 / 13卷

关键词：

Density function; electromagnetics; magnetic resonance imaging (MRI); uncertainty quantification; POLYNOMIAL-CHAOS; UNCERTAINTY QUANTIFICATION; CIRCUITS;

D O I：

10.1109/LAWP.2014.2349933

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：1656 / 1659

页数：4

共 21 条

[1]

[Anonymous], ARXIV14073023

[2] Efficient Analysis of Geometrical Uncertainty in the FDTD Method Using Polynomial Chaos With Application to Microwave Circuits [J].