[1] [1] ANDRE J B, BRESNAHAN B W, BASHA M M, et al. Toward quantifying the prevalence, severity, and cost associated with patient motion during clinical MR examinations[J]. Journal of the American college of radiology, 2015, 12(7):689-695.

[2] [2] GODENSCHWWGWR F, KAGEBEIN U, STUCHT D, et al. Motion correction in MRI of the brain[J]. Physics in medicine & biology, 2016, 61(5):R32-R56.

[3] [3] VAN DER KOUWE A J W, BENNER T, DALE A M. Real-time rigid body motion correction and shimming using cloverleaf navigators[J]. Magnetic resonance in medicine:an official journal of the international society for magnetic resonance in medicine, 2006, 56(5): 1019-1032.

[4] [4] WELCH E B, MANDUCA A, GRIMM R C, et al. Spherical navigator echoes for full 3D rigid body motion measurement in MRI[J]. Magnetic resonance in medicine:an official journal of the international society for magnetic resonance in medicine, 2002, 47(1):32-41.

[5] [5] LYU Q, SHAN H, XIE Y, et al. Cine cardiac MRI motion artifact reduction using a recurrent neural network[EB/OL]. (2020-06-23)[2021-09-20]. https://arxiv.org/abs/2006.12700v1.

[6] [6] TAMADA D, KROMREY M L, ICHIKAWA S, et al. Motion artifact reduction using a convolutional neural network for dynamic contrast enhanced MR imaging of the liver[J]. Magnetic resonance in medical sciences, 2020, 19(1):64-76.

[7] [7] ZHANG K, ZUO W, CHEN Y, et al. Beyond a Gaussian denoiser:residual learning of deep CNN for image denoising[J]. IEEE transactions on image processing, 2017, 26(7):3142-3155.

[8] [8] PAWAR K, CHEN Z, SHAH N J, et al. MoCoNet: motion correction in 3D MPRAGE images using a convolutional neural network approach[EB/OL]. (2018-07-29) [2021-09-20]. https://arxiv.org/abs/1807.10831v1.

[9] [9] LIU J, KOCAK M, SUPANICH M, et al. Motion artifacts reduction in brain MRI by means of a deep residual network with densely connected multi-resolution blocks (DRN-DCMB)[J]. Magnetic resonance imaging, 2020, 71:69-79.

[10] [10] RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), October 5-9, 2015, Munich, Germany. Berlin, Heidelberg:Springer-Verlag, 2015:234-241.

[11] [11] WOO S, PARK J, LEE J Y, et al. CBAM : convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV), September 8-14, 2018, Munich, Germany. Berlin, Heidelberg:Springer-Verlag, 2018:3-19.

[12] [12] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York:IEEE, 2016:770-778.

[13] [13] HE K, ZHANG X, REN S, et al. Delving deep into rectifiers : surpassing human-level performance on imagenet classification[C]//Proceedings of the IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York:IEEE, 2015:1026-1034.

[14] [14] KINGMA D P, BA J. Adam:a method for stochastic optimization[EB/OL]. (2017-06-30)[2021-09-20]. https://arxiv.org/pdf/1412.6980.pdf.

[15] [15] ZHAO H, GALLO O, FROSIO I, et al. Loss functions for image restoration with neural networks[J]. IEEE transactions on computational imaging, 2016, 3(1):47-57.

[16] [16] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York:IEEE, 2015:3431-3440.