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 journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 73-77 |
Journal / Publication | IEEE Transactions on Medical Imaging |
Volume | 12 |
Issue number | 1 |
Publication status | Published - Mar 1993 |
Externally published | Yes |
Link(s)
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
Citation Format(s)
Data Truncation Artifact Reduction in MR Imaging Using a Multilayer Neural Network. / Yan, Hong; Mao, Jintong.
In: IEEE Transactions on Medical Imaging, Vol. 12, No. 1, 03.1993, p. 73-77.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review