Reduction of Gibbs artifacts in magnetic resonance imaging based on Convolutional Neural Network

Abstract

In Magnetic Resonance Imaging (MRI), the K-space data is often under-sampled and truncated to shorten the scan time. However, the truncation of K-space also causes Gibbs ringing artifacts in the image, which seriously deteriorates the image quality. Inspired by the recent achievements of deep learning, we propose a novel method to reduce Gibbs artifacts in MRI with Convolutional Neural Network (CNN) in this paper. CNN is trained with a batch of image pairs with and without Gibbs artifacts. Afterwards, images with Gibbs artifacts can be input into the trained network to get the Gibbs-free images. Output of CNN is then transformed into K-space and merged with the sampled K-space data. Finally, inverse Fourier transform is applied to the merged K-space to get the final image. Experiments on both phantoms and real MRI images proved that the proposed method could reduce the Gibbs artifacts to a great degree and keep more image details compared with traditional Tukey filter.