Title :
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
fDate :
3/1/1993 12:00:00 AM
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;
Journal_Title :
Medical Imaging, IEEE Transactions on
