Open AccessFast eddy current compensation by feedback linearization neural networks: Applications in diffusion‐weighted echo planar imaging
Author(s) -
Hwang SanChao,
Yao Ching,
Hsieh ChaoHsien,
Chen JyhHorng
Publication year - 2006
Publication title -
concepts in magnetic resonance part b: magnetic resonance engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.286
H-Index - 32
eISSN - 1552-504X
pISSN - 1552-5031
DOI - 10.1002/cmr.b.20058
Subject(s) - linearization , echo planar imaging , compensation (psychology) , artificial neural network , distortion (music) , computer science , eddy current , feedback linearization , control theory (sociology) , planar , diffusion , current (fluid) , algorithm , artificial intelligence , nonlinear system , physics , telecommunications , control (management) , medicine , psychology , amplifier , computer graphics (images) , bandwidth (computing) , quantum mechanics , magnetic resonance imaging , psychoanalysis , radiology , thermodynamics
This study describes the application of the method of feedback linearization neural networks, known from neural network computing, to the problem of gradient preemphasis. This approach of preemphasis adjustment does not require an iterative procedure between measurement and adjustment, therefore is essentially instantaneous in its execution. Based on our study, gradient compensation determined by our procedure effectively suppressed eddy current‐induced geometric distortion and spatial shift of diffusion‐weighted EPI images. © 2006 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 29B: 1–8, 2006