Reducing magnetic resonance image spacing by learning without ground-truth

Kai Xuan, Liping Si, Lichi Zhang, Zhong Xue, Yining Jiao, Weiwu Yao, Dinggang Shen, Dijia Wu, Qian Wang

Abstract: Highlights•MR images are usually acquired with large inter-slice spacing in clinical practice.•A deep-learning-based algorithm to reduce slice spacing for better rendering.•No real high-resolution ground-truth is required for training neural-networks.•Outperform current self super-resolution algorithms for MR images.

External IDs:doi:10.1016/j.patcog.2021.108103