Convolutional Neural Networks with Aliasing Layers for Correcting Parallel Imaging and EPI Ghost Artifacts

The author proposes a new layer named aliasing layer (AL) for effectively correcting MR-specific aliasing artifacts using convolutional neural networks. In MR images acquired using parallel imaging (PI) and/or echo-planar imaging (EPI), the locations of aliasing artifacts and/or N/2 ghost artifacts can be analytically calculated. The AL preprocesses MR images by moving the calculated locations to the locations accessible through summations over all channels in a convolution layer. The experimental results demonstrate that the correction method using the proposed AL could effectively remove PI aliasing and EPI ghosting artifacts.