Aliasing layers for processing parallel imaging and EPI ghost artifacts efficiently in convolutional neural networks.

Abstract

The purposes of this work are to develop a method for efficiently processing MR-specific artifacts using a convolutional neural network (CNN), and to present its applications for the removal of the artifacts without suppressing actual signals. In MR images that are acquired using parallel imaging and/or EPI, the locations of aliasing artifacts and/or N-half ghost artifacts can be analytically calculated. However, existing methods using CNNs do not take the structures of the artifacts into account, and therefore need a large number of convolution layers for processing the artifacts. For processing the artifacts, a new layer that is named the aliasing layer (AL) is proposed. Because a CNN stands on the assumption that an image has spatial locality, a convolution layer is formulated as a linear function of neighbor locations. For processing the artifacts, the AL preprocesses MR images by moving the calculated locations to the locations accessible through summations over all channels in a standard convolution layer. To evaluate the application of ALs for the removal of parallel imaging and EPI artifacts, CNNs with ALs were compared with those without ALs. The results showed that image-quality metrics of a six-layer CNN with ALs were better than those of a 12-layer CNN without ALs. The results also showed that CNNs with ALs suppressed the artifacts selectively. The aliasing layer is proposed for processing MR-specific artifacts efficiently. The experimental results demonstrated that the AL improved CNNs for removing artifacts from parallel imaging and EPI.