Gibbs-ringing artifact suppression with knowledge transfer from natural images to MR images

Gibbs-ringing is a common artifact in magnetic resonance imaging (MRI), which is mainly caused by the finite k-space sampling and the truncation of high frequency (HF) information at the sampling border. It is especially visible for imaging acquisitions at low resolution and can be typically suppressed by filtering at the expense of further loss of HF components. As a classic image restoration problem in MRI, Gibbs-ringing artifact suppression can be viewed as a typical ill-posed inverse problem of image generation in computer vision community, such as image super-resolution and inpainting. Inspired by this, the present work presents a novel method to suppress the Gibbs-ringing artifacts with knowledge transfer from natural images to MR images. The highly nonlinear relation between the artifact-degraded image and the corresponding artifact-free counterpart is modeled with a typical convolutional neural network (CNN), which is first trained with natural images and then fine-tuned with MR images. Unlike many other works, we use transfer learning between different types of images to deal with regression problems, rather than classification problems. The experimental results exhibit that there exists information sharing between natural images and MR images with regard to the same problem, and the knowledge learned from natural images can indeed improve the performance of regression models on MR images.