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

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.

[1]  Tae Hyun Kim,et al.  Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Rishi Sharma,et al.  A Note on the Inception Score , 2018, ArXiv.

[3]  Jean Ponce,et al.  Learning a convolutional neural network for non-uniform motion blur removal , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Kyoung Mu Lee,et al.  Accurate Image Super-Resolution Using Very Deep Convolutional Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Shubham Pachori,et al.  Deep Generative Filter for Motion Deblurring , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

[6]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[8]  Uma Shanker Tiwary,et al.  An Adaptively Accelerated Lucy-Richardson Method for Image Deblurring , 2008, EURASIP J. Adv. Signal Process..

[9]  Yi Wang,et al.  Scale-Recurrent Network for Deep Image Deblurring , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[10]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[11]  Kyoung Mu Lee,et al.  Enhanced Deep Residual Networks for Single Image Super-Resolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[12]  Wu Chunli,et al.  An improved algorithm of translational motion artifact correction for MRI , 2015, The 27th Chinese Control and Decision Conference (2015 CCDC).

[13]  Vladlen Koltun,et al.  Photographic Image Synthesis with Cascaded Refinement Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[14]  Jiri Matas,et al.  DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[15]  Jenq-Neng Hwang,et al.  Motion artifact correction of MRI via iterative inverse problem solving , 1994, Proceedings of 1st International Conference on Image Processing.

[16]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[18]  拓海 杉山,et al.  “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .

[19]  Bradley P. Sutton,et al.  K-space and image space combination for motion artifact correction in multicoil multishot diffusion weighted imaging , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[20]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Huang Mi.  Research and realization of correction method of MRI motion artifact , 2013 .

[22]  Kangfu Mei,et al.  Multi-scale Residual Network for Image Super-Resolution , 2018, ECCV.

[23]  Li Xu,et al.  Unnatural L0 Sparse Representation for Natural Image Deblurring , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.