Data Truncation Artifact Reduction in MR Imaging Using a Multilayer Neural Network

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)73-77
Journal / PublicationIEEE Transactions on Medical Imaging
Volume12
Issue number1
Publication statusPublished - Mar 1993
Externally publishedYes

Abstract

A magnetic resonance image may contain truncation artifacts if there are not enough high-frequency data when the conventional Fourier transform method is used for reconstruction. In this paper we propose a method for reducing the artifacts using a multilayer neural network. The network consists of one linear output layer and at least one nonlinear hidden layer. In this method the missing high-frequency components are predicted based on known low-frequency components and are used to reduce the truncation artifacts of the image. The method is tested with simulated data with good results. © 1993 IEEE