Title :
MR image resolution enhancement using a multi-layer neural network
Author :
Yan, Hong ; Mao, Jintong ; Chen, Benjamin
Author_Institution :
Sch. of Electr. Eng., Sydney Univ., NSW, Australia
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. The authors 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 improve the resolution of the image. The method is tested with simulated data with good results
Keywords :
biomedical NMR; medical image processing; neural nets; Fourier transform method; high-frequency components; linear output layer; low-frequency components; magnetic resonance image; multilayer neural network; nonlinear hidden layer; resolution enhancement; truncation artifacts; Fourier transforms; Frequency; Image coding; Image reconstruction; Image resolution; Multi-layer neural network; Neural networks; Predictive models; Signal resolution; Testing;
Conference_Titel :
Computer-Based Medical Systems, 1992. Proceedings., Fifth Annual IEEE Symposium on
Conference_Location :
Durham, NC
Print_ISBN :
0-8186-2742-5
DOI :
10.1109/CBMS.1992.245027
