SEGAN: Structure-Enhanced Generative Adversarial Network for Compressed Sensing MRI Reconstruction

Generative Adversarial Networks (GANs) are powerful tools for reconstructing Compressed Sensing Magnetic Resonance Imaging (CS-MRI). However most recent works lack exploration of structure information of MRI images that is crucial for clinical diagnosis. To tackle this problem, we propose the Structure-Enhanced GAN (SEGAN) that aims at restoring structure information at both local and global scale. SEGAN defines a new structure regularization called Patch Correlation Regularization (PCR) which allows for efficient extraction of structure information. In addition, to further enhance the ability to uncover structure information, we propose a novel generator SU-Net by incorporating multiple-scale convolution filters into each layer. Besides, we theoretically analyze the convergence of stochastic factors contained in training process. Experimental results show that SEGAN is able to learn target structure information and achieves state-of-theart performance for CS-MRI reconstruction.

[1]  Daniel K Sodickson,et al.  Low‐rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components , 2015, Magnetic resonance in medicine.

[2]  M. Defrise,et al.  Image reconstruction. , 2006, Physics in medicine and biology.

[3]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[4]  Huchuan Lu,et al.  Visual tracking via adaptive structural local sparse appearance model , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Di Guo,et al.  Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform , 2016, Medical Image Anal..

[6]  David Atkinson,et al.  Dynamic MR Image Reconstruction–Separation From Undersampled (${\bf k},t$)-Space via Low-Rank Plus Sparse Prior , 2014, IEEE Transactions on Medical Imaging.

[7]  Martin Zinkevich,et al.  Online Convex Programming and Generalized Infinitesimal Gradient Ascent , 2003, ICML.

[8]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .

[9]  Di Guo,et al.  Fast Multiclass Dictionaries Learning With Geometrical Directions in MRI Reconstruction , 2015, IEEE Transactions on Biomedical Engineering.

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

[11]  Daniel Rueckert,et al.  Dictionary Learning and Time Sparsity for Dynamic MR Data Reconstruction , 2014, IEEE Transactions on Medical Imaging.

[12]  Won-Ki Jeong,et al.  Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss , 2017, IEEE Transactions on Medical Imaging.

[13]  Jian Sun,et al.  ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI , 2017, ArXiv.

[14]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[15]  Junzhou Huang,et al.  Accelerated Dynamic MRI Reconstruction with Total Variation and Nuclear Norm Regularization , 2015, MICCAI.

[16]  Daniel Rueckert,et al.  A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction , 2017, IPMI.

[17]  Leon Kaufman,et al.  A general algorithm for oblique image reconstruction , 1990, IEEE Computer Graphics and Applications.

[18]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Kieren Grant Hollingsworth,et al.  Reducing acquisition time in clinical MRI by data undersampling and compressed sensing reconstruction , 2015, Physics in medicine and biology.

[20]  Chuan Zhou,et al.  Deep Learning in Medical Image Analysis. , 2017, Advances in experimental medicine and biology.

[21]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[22]  Christoph Meinel,et al.  Deep Learning for Medical Image Analysis , 2018, Journal of Pathology Informatics.

[23]  Guang Yang,et al.  DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction , 2018, IEEE Transactions on Medical Imaging.

[24]  Jong Chul Ye,et al.  Deep residual learning for compressed sensing MRI , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[25]  Tom Goldstein,et al.  The Split Bregman Method for L1-Regularized Problems , 2009, SIAM J. Imaging Sci..

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

[27]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[28]  Danna Zhou,et al.  d. , 1934, Microbial pathogenesis.

[29]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[31]  D. Donoho,et al.  Sparse MRI: The application of compressed sensing for rapid MR imaging , 2007, Magnetic resonance in medicine.

[32]  Thomas Pock,et al.  Learning a variational network for reconstruction of accelerated MRI data , 2017, Magnetic resonance in medicine.

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

[34]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[35]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.