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[1] P. Lauterbur,et al. Image Formation by Induced Local Interactions: Examples Employing Nuclear Magnetic Resonance , 1973, Nature.
[2] Tao Tan,et al. Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network , 2017, Japanese Journal of Radiology.
[3] Kannan Ramchandran,et al. Multiplexed coded illumination for Fourier Ptychography with an LED array microscope. , 2014, Biomedical optics express.
[4] C K Hitzenberger,et al. Spectral measurement of absorption by spectroscopic frequency-domain optical coherence tomography. , 2000, Optics letters.
[5] Lawrence L. Wald,et al. TArgeted Motion Estimation and Reduction (TAMER): Data Consistency Based Motion Mitigation for MRI Using a Reduced Model Joint Optimization , 2018, IEEE Transactions on Medical Imaging.
[6] Robert D. Nowak,et al. Wavelet-based Rician noise removal for magnetic resonance imaging , 1999, IEEE Trans. Image Process..
[7] S. Kevin Zhou,et al. DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction With Deep T1 Prior , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[8] C. Jack,et al. Alzheimer's Disease Neuroimaging Initiative , 2008 .
[9] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[10] Alexei A. Efros,et al. The Unreasonable Effectiveness of Deep Features as a Perceptual Metric , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[11] Steen Moeller,et al. Scan‐specific robust artificial‐neural‐networks for k‐space interpolation (RAKI) reconstruction: Database‐free deep learning for fast imaging , 2018, Magnetic resonance in medicine.
[12] Pascal Vincent,et al. fastMRI: An Open Dataset and Benchmarks for Accelerated MRI , 2018, ArXiv.
[13] M. Lustig,et al. SPIRiT: Iterative self‐consistent parallel imaging reconstruction from arbitrary k‐space , 2010, Magnetic resonance in medicine.
[14] Arthur W. Toga,et al. Retrospective correction of motion artifact affected structural MRI images using deep learning of simulated motion , 2018 .
[15] Jin Keun Seo,et al. Deep learning for undersampled MRI reconstruction , 2017, Physics in medicine and biology.
[16] J. Sahambi,et al. Wavelet domain non-linear filtering for MRI denoising. , 2010, Magnetic resonance imaging.
[17] Tammy Riklin-Raviv,et al. Ensemble of expert deep neural networks for spatio‐temporal denoising of contrast‐enhanced MRI sequences , 2017, Medical Image Anal..
[18] Daniel Rueckert,et al. A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction , 2017, IEEE Transactions on Medical Imaging.
[19] Guang Yang,et al. DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction , 2018, IEEE Transactions on Medical Imaging.
[20] José V. Manjón,et al. MRI denoising using Non-Local Means , 2008, Medical Image Anal..
[21] Thomas Pock,et al. Learning a variational network for reconstruction of accelerated MRI data , 2017, Magnetic resonance in medicine.
[22] A. Macovski. Noise in MRI , 1996, Magnetic resonance in medicine.
[23]
Jong Chul Ye,et al.
[24] P. Batchelor,et al. Matrix description of general motion correction applied to multishot images , 2005, Magnetic resonance in medicine.
[25] Bruce R. Rosen,et al. Image reconstruction by domain-transform manifold learning , 2017, Nature.
[26] Justin P. Haldar,et al. LORAKI: Autocalibrated Recurrent Neural Networks for Autoregressive MRI Reconstruction in k-Space , 2019, ArXiv.
[27] Kawin Setsompop,et al. Network Accelerated Motion Estimation and Reduction (NAMER): Convolutional neural network guided retrospective motion correction using a separable motion model , 2019, Magnetic resonance in medicine.
[28] Pierrick Coupé,et al. MRI Denoising Using Deep Learning , 2018, Patch-MI@MICCAI.
[29] Eero P. Simoncelli,et al. Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.
[30] Mathews Jacob,et al. MoDL: Model-Based Deep Learning Architecture for Inverse Problems , 2017, IEEE Transactions on Medical Imaging.
[31] Pablo Irarrazaval,et al. Noise in magnitude magnetic resonance images , 2008 .
[32] Robin M Heidemann,et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA) , 2002, Magnetic resonance in medicine.
[33] Zhaolin Chen,et al. MoCoNet: Motion Correction in 3D MPRAGE images using a Convolutional Neural Network approach , 2018, ArXiv.
[34] P. Boesiger,et al. SENSE: Sensitivity encoding for fast MRI , 1999, Magnetic resonance in medicine.
[35] Mohammed Ghanbari,et al. Scope of validity of PSNR in image/video quality assessment , 2008 .
[36] Jian Sun,et al. Deep ADMM-Net for Compressive Sensing MRI , 2016, NIPS.
[37] Junaid Qadir,et al. Retrospective Motion Correction in Multishot MRI using Generative Adversarial Network , 2019, Scientific Reports.
[38] Zhou Wang,et al. Multiscale structural similarity for image quality assessment , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.
[39] Zhaolin Chen,et al. Suppressing motion artefacts in MRI using an Inception‐ResNet network with motion simulation augmentation , 2019, NMR in biomedicine.
[40] Yoshimi Anzai,et al. Toward Quantifying the Prevalence, Severity, and Cost Associated With Patient Motion During Clinical MR Examinations. , 2015, Journal of the American College of Radiology : JACR.
[41] Won-Ki Jeong,et al. Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss , 2017, IEEE Transactions on Medical Imaging.
[42] Patrick Putzky,et al. Invert to Learn to Invert , 2019, NeurIPS.
[43] Jong Chul Ye,et al. Deep residual learning for compressed sensing MRI , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).
[44] Mert R. Sabuncu,et al. Deep-Learning-Based Optimization of the Under-Sampling Pattern in MRI , 2020, IEEE Transactions on Computational Imaging.
[45] M. Lustig,et al. Compressed Sensing MRI , 2008, IEEE Signal Processing Magazine.
[46] Bin Yang,et al. Retrospective correction of motion‐affected MR images using deep learning frameworks , 2019, Magnetic resonance in medicine.
[47] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).