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

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.