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• Technical Articles •
Reconstruction of under-sampled magnetic resonance image based on convolutional neural network
WANG Yi-da  SONG Yang  XIE Hai-bin  TONG Rui  LI Jian-qi  YANG Guang 

DOI:10.12015/issn.1674-8034.2018.06.010.


[Abstract] Objective: To reconstruct high quality, artifacts-free magnetic resonance imaging (MRI) images from under-sampled k-space data with convolutional neural network (CNN).Materials and Methods: T1-weighted brain MR images of sagittal, transverse and coronal orientations from sixty volunteers are used. Rotation and stretching were used for data augmentation. CNN was trained with pairs of ground truth and under-sampled MR images to learn the nonlinear mapping between them. In the reconstruction, output of CNN was merged with the sampled k-space data to get the final image. For quantitative evaluation, we used peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and high-frequency error norm (HFEN) to compare results of different methods.Results: (1) PSNR, SSIM, HFEN of center-sampled MR images reconstructed by CNN are 31.13, 0.93, 223.81, compared with 25.69, 0.86, 482.75 of Tukey filter. (2) PSNR, SSIM, HFEN of pseudo-random sampled MR images reconstructed by CNN are 32.78, 0.95, 195.51, compared with 31.01, 0.93, 184.69 of compressed sensing.Conclusions: CNN can reconstruct high quality MR images from under-sampled data and achieved better results both visually and statistically, compared with traditional methods. For CNN-based reconstruction, pseudo-random sampling is more favorable.
[Keywords] Magnetic resonance imaging；Convolutional neural network；Image reconstruction；Under-sampling；Compressed sensing

WANG Yi-da School of Physics & Materials Science, East China Normal University, Shanghai Key Laboratory of Magnetic Resonance, Shanghai 200062, China

SONG Yang School of Physics & Materials Science, East China Normal University, Shanghai Key Laboratory of Magnetic Resonance, Shanghai 200062, China

XIE Hai-bin School of Physics & Materials Science, East China Normal University, Shanghai Key Laboratory of Magnetic Resonance, Shanghai 200062, China

TONG Rui School of Physics & Materials Science, East China Normal University, Shanghai Key Laboratory of Magnetic Resonance, Shanghai 200062, China

LI Jian-qi School of Physics & Materials Science, East China Normal University, Shanghai Key Laboratory of Magnetic Resonance, Shanghai 200062, China

YANG Guang* School of Physics & Materials Science, East China Normal University, Shanghai Key Laboratory of Magnetic Resonance, Shanghai 200062, China

*Correspondence to: Yang G, E-mail: gyang@phy.ecnu.edu.cn

Conflicts of interest   None.

ACKNOWLEDGMENTS  This work was part of Key Program of Natural Science Foundation of China No.61731009
Received  2018-04-11
Accepted  2018-04-24
DOI: 10.12015/issn.1674-8034.2018.06.010
DOI:10.12015/issn.1674-8034.2018.06.010.

[1]
Mcgibney G, Smith MR, Nichols ST, et al. Quantitative evaluation of several partial Fourier reconstruction algorithms used in MRI. Magn Reson Med, 1993, 30(1): 51-59.
[2]
Pruessmann KP, Markus W, Scheidegger MB, et al. SENSE: sensitivity encoding for fast MRI. Magn Reson Med, 1999, 42(5): 952-962.
[3]
Griswold MA, Jakob PM, Heidemann RM, et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med, 2002, 47(6): 1202-1210.
[4]
Lustig M, Donoho D, Pauly JM. Sparse MRI: The application of compressed sensing for rapid MR imaging. Magn Reson Med, 2007, 58(6): 1182.
[5]
Wood ML, Henkelman RM. Truncation artifacts in magnetic resonance imaging. Magn Reson Med, 1985, 2(6): 517-526.
[6]
Harris FJ. On the use of windows for harmonic analysis with the discrete fourier transform. IEEE Proceedings, 1978, 66(1): 51-83.
[7]
Ye J, Qu X, Guo H, et al. Patch-based directional redundant wavelets in compressed sensing parallel magnetic resonance imaging with radial sampling trajectory. J Med Imaging & Health Inform, 2016, 6(2): 387-398.
[8]
Liang D, Liu B. Accelerating SENSE using compressed sensing. Magn Reson Med, 2009, 62(6): 1574-1584.
[9]
Chang Y, Liang D, Ying L. Nonlinear GRAPPA: a kernel approach to parallel MRI reconstruction. Magn Reson Med, 2012, 68(3): 730.
[10]
Pereira S, Pinto A, Alves V, et al. Brain tumor segmentation using convolutional neural networks in MR images. IEEE Trans Med Imaging, 2016, 35(5): 1240-1251.
[11]
Shin HC, Roth HR, Gao M, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging, 2016, 35(5): 1285-1298.
[12]
Bahrami K, Shi F, Zong X, et al. Reconstruction of 7T-like Images from 3 T MRI. IEEE Trans Med Imaging, 2016, 35(9): 2085-2097.
[13]
Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2015: 234-241.
[14]
Glorot X, Bordes A, Bengio Y, et al. Deep sparse rectifier neural networks. Jmlr W & Cp, 2012, 15: 315-323.
[15]
Lecun Y, Boser B, Denker JS, et al. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1989, 1(4): 541-551.
[16]
Hore A, Ziou D. Image quality metrics: PSNR vs. SSIM//International Conference on Pattern Recognition. IEEE, 2010: 2366-2369.
[17]
Ravishankar S, Bresler Y. MR image reconstruction from highly undersampled k-space data by dictionary learning. IEEE Trans Med Imaging, 2011, 30(5): 1028-1041.
[18]
Goldstein T, Osher S. The split bregman method for L1-regularized problems. Society for Industrial and Applied Mathematics, 2009, 2(2): 323-343.
[19]
Lai Z, Qu X, Liu Y, et al. Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform. Med Image Anal, 2016, 27(1): 93-104.

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