In MRI, metallic implants can generate magnetic field distortions and interfere in the spatial encoding of gradient magnetic fields. This results in image distortions, such as bulk shifts, pile-up and signal-loss artifacts. Three-dimensional spectral imaging methods can reduce the bulk shifts to a single-voxel level, but they still suffer from residual artifacts such as pile-up and signal-loss artifacts. Fully phase encoding methods suppress metal-induced artifacts, but they require impractically long imaging times. In this paper, we applied a deep learning method to correct metal artifacts. A neural network is proposed to map two distorted images obtained by dual-polarity readout gradients into a distortion-free image obtained by fully phase encoding. Simulated data were utilized to supplement and substitute real MR data for training the proposed network. Phantom experiments were performed to compare the quality of reconstructed images from several methods at high and low readout bandwidths.
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