Multi-Scale Network with the Deeper and Wider Residual Block for MRI Motion Artifact Correction

摘要

Magnetic resonance imaging (MRI) motion artifact is common in clinic which affects the doctor to accurately locate the lesion and diagnose the condition. MRI motion artifact is caused by the physiological movements of the patient while scanning the organ. Most of the current methods do artifact suppression and image restoration on the inverse Fourier transform level. They are neither effective nor efficient and can not be utilized in clinic. In this paper, the method that transfers deep learning into this domain with adopting a novel approach in Multi-scale mechanism for MRI motion artifact correction was proposed. What' more, a newer residual block with the deeper and wider architecture was proposed. With the deeper and wider residual block, the correction effect is greatly improved. The Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) were adopted as the evaluation metrics. In short, our model is trainable in an end-to-end network, can be tested in real-time and achieves the state-of-the-art results for MRI motion artifact correction.