A Structural Oriented Training Method for GAN Based Fast Compressed Sensing MRI

摘要

Traditional strategies for reconstructing Compressed Sensing Magnetic Resonance Imaging (CS-MRI) may introduce computational redundancy, and deep learning-based methods can significantly reduce reconstruction time and improve restoration quality. However, many recent deep learning-based algorithms lay insufficient attention to spatial frequency information. In this paper, a Structural Oriented Generative Adversarial Network (SOGAN) is proposed aiming at restoring image domain information as well as refining frequency domain during the reconstruction of CS-MRI. Numerical Experiments showed our model's efficiency and capability for diagnostic purpose.