Abstract

Suppression of ringing artifacts in images is a well-known image restoration problem. Gibbs-ringing artifacts occur when, in the process of magnetic resonance imaging, the source data from the frequency domain are mapped onto the spatial domain by using the discrete Fourier transform. The artifacts are caused by the incompleteness of these data, which, in turn, is due to cutting off the high frequencies of the Fourier spectrum. In this paper, we propose a hybrid method for Gibbs-ringing artifact suppression in magnetic resonance images that combines a deep learning model and a classical non-machine-learning algorithm for Gibbs-ringing artifact suppression based on optimal subvoxel shifts.

中文翻译：

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磁共振图像中吉布斯振铃伪影抑制的混合方法

摘要

图像中振铃伪影的抑制是一个众所周知的图像恢复问题。在磁共振成像过程中，当使用离散傅立叶变换将来自频域的源数据映射到空间域时，就会出现吉布斯振铃伪影。伪影是由这些数据的不完整性引起的，而这又是由于切断了傅立叶频谱的高频。在本文中，我们提出了一种混合方法来抑制磁共振图像中的吉布斯环伪影，该方法结合了深度学习模型和经典的非机器学习算法，用于基于最佳亚体素位移的吉布斯环伪影抑制。