Data truncation artifact reduction in MR imaging using a multilayer neural network

Author

Yan, Hong ; Mao, Jintong

Author_Institution

Dept. of Electr. Eng., Sydney Univ., NSW, Australia

Volume

12

Issue

1

fYear

1993

fDate

3/1/1993 12:00:00 AM

Firstpage

73

Lastpage

77

Abstract

A magnetic resonance image (MRI) may contain truncation artifacts if there are not enough high-frequency data when the conventional Fourier transform method is used for reconstruction. A method for reducing the artifacts using a multilayer neural network is presented. The network consists of one linear output layer and at least one nonlinear hidden layer. The missing high-frequency components are predicted based on known low-frequency components and are used to reduce the truncation artifacts of the image. Results from a series of simulation experiments are discussed

Keywords

biomedical NMR; medical image processing; neural nets; Fourier transform based reconstruction; MR imaging; data truncation artifact reduction; known low-frequency components; linear output layer; magnetic resonance image; medical diagnostic imaging; missing high-frequency; multilayer neural network; nonlinear hidden layer; simulation experiments; Encoding; Fourier transforms; Frequency domain analysis; Intelligent networks; Magnetic multilayers; Magnetic resonance; Magnetic resonance imaging; Multi-layer neural network; Neural networks; Testing;

fLanguage

English

Journal_Title

Medical Imaging, IEEE Transactions on

Publisher

ieee

ISSN

0278-0062

Type

jour

DOI

10.1109/42.222669

Filename

222669