Deep Learning in MR Image Processing

[1]  Robin M Heidemann,et al.  Generalized autocalibrating partially parallel acquisitions (GRAPPA) , 2002, Magnetic resonance in medicine.

[2]  Michael Elad,et al.  Convolutional Neural Networks Analyzed via Convolutional Sparse Coding , 2016, J. Mach. Learn. Res..

[3]  J. Duerk,et al.  Magnetic Resonance Fingerprinting , 2013, Nature.

[4]  Jong Chul Ye,et al.  Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems , 2017, SIAM J. Imaging Sci..

[5]  Richard Kijowski,et al.  Deep convolutional neural network for segmentation of knee joint anatomy , 2018, Magnetic resonance in medicine.

[6]  Yi Wang,et al.  Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: Validation and application to brain imaging , 2010, Magnetic resonance in medicine.

[7]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.

[8]  Yi Wang,et al.  Calculation of susceptibility through multiple orientation sampling (COSMOS): A method for conditioning the inverse problem from measured magnetic field map to susceptibility source image in MRI , 2009, Magnetic resonance in medicine.

[9]  Xiaoou Tang,et al.  Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Bo Zhu,et al.  MR fingerprinting Deep RecOnstruction NEtwork (DRONE) , 2017, Magnetic resonance in medicine.

[11]  Yang Song,et al.  Computer‐aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI , 2018, Journal of magnetic resonance imaging : JMRI.

[12]  Thomas Wiatowski,et al.  A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction , 2015, IEEE Transactions on Information Theory.

[13]  R. Bowtell,et al.  Susceptibility mapping in the human brain using threshold‐based k‐space division , 2010, Magnetic resonance in medicine.

[14]  David Atkinson,et al.  Automatic correction of motion artifacts in magnetic resonance images using an entropy focus criterion , 1997, IEEE Transactions on Medical Imaging.

[15]  Roland Kreis,et al.  Deep learning approaches for detection and removal of ghosting artifacts in MR spectroscopy , 2018, Magnetic resonance in medicine.

[16]  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.

[17]  Bruce R. Rosen,et al.  Image reconstruction by domain-transform manifold learning , 2017, Nature.

[18]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[19]  Jong Chul Ye,et al.  Deep learning with domain adaptation for accelerated projection‐reconstruction MR , 2018, Magnetic resonance in medicine.

[20]  Kawin Setsompop,et al.  Quantitative susceptibility mapping using deep neural network: QSMnet , 2018, NeuroImage.

[21]  Shaohui Liu,et al.  Medical image denoising using convolutional neural network: a residual learning approach , 2017, The Journal of Supercomputing.

[22]  Taeseong Kim,et al.  KIKI‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images , 2018, Magnetic resonance in medicine.

[23]  Jeff Wood,et al.  Super‐resolution musculoskeletal MRI using deep learning , 2018, Magnetic resonance in medicine.

[24]  Mathews Jacob,et al.  MoDL: Model-Based Deep Learning Architecture for Inverse Problems , 2017, IEEE Transactions on Medical Imaging.

[25]  Christian Igel,et al.  Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network , 2013, MICCAI.

[26]  Bernhard Schölkopf,et al.  Blind retrospective motion correction of MR images , 2012, Magnetic resonance in medicine.

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

[28]  Jeffrey A. Fessler,et al.  Image Reconstruction is a New Frontier of Machine Learning , 2018, IEEE Transactions on Medical Imaging.

[29]  Dong-Hyun Kim,et al.  Data‐driven synthetic MRI FLAIR artifact correction via deep neural network , 2019, Journal of magnetic resonance imaging : JMRI.

[30]  Kanghyun Ryu,et al.  Synthesizing T1 weighted MPRAGE image from multi echo GRE images via deep neural network. , 2019, Magnetic resonance imaging.

[31]  Ki Hwan Kim,et al.  Improving resolution of MR images with an adversarial network incorporating images with different contrast , 2018, Medical physics.

[32]  Victor Alves,et al.  Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images , 2016, IEEE Transactions on Medical Imaging.

[33]  L. Schad,et al.  Diffusion parameter mapping with the combined intravoxel incoherent motion and kurtosis model using artificial neural networks at 3 T , 2017, NMR in biomedicine.

[34]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[35]  Wei Li,et al.  Prostate cancer diagnosis using deep learning with 3D multiparametric MRI , 2017, Medical Imaging.

[36]  Sebastian Weingärtner,et al.  Oxygen extraction fraction mapping at 3 Tesla using an artificial neural network: A feasibility study , 2018, Magnetic resonance in medicine.

[37]  Oliver Speck,et al.  Measurement and Correction of Microscopic Head Motion during Magnetic Resonance Imaging of the Brain , 2012, PloS one.

[38]  Daniel K Sodickson,et al.  Assessment of the generalization of learned image reconstruction and the potential for transfer learning , 2019, Magnetic resonance in medicine.

[39]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Konstantinos Kamnitsas,et al.  Multi-scale 3D convolutional neural networks for lesion segmentation in brain MRI , 2015 .

[41]  Tobias Kober,et al.  Head motion detection using FID navigators , 2011, Magnetic resonance in medicine.

[42]  T. Naidich,et al.  Synthetic MRI for Clinical Neuroimaging: Results of the Magnetic Resonance Image Compilation (MAGiC) Prospective, Multicenter, Multireader Trial , 2017, American Journal of Neuroradiology.

[43]  Daniel L. Rubin,et al.  Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions , 2017, Journal of Digital Imaging.

[44]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[45]  Xuanqin Mou,et al.  Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss , 2017, IEEE Transactions on Medical Imaging.

[46]  Shihui Ying,et al.  Super-resolution reconstruction of MR image with a novel residual learning network algorithm , 2018, Physics in medicine and biology.

[47]  Yi Wang,et al.  Morphology enabled dipole inversion (MEDI) from a single‐angle acquisition: Comparison with COSMOS in human brain imaging , 2011, Magnetic resonance in medicine.

[48]  Morteza Mardani,et al.  Deep Generative Adversarial Neural Networks for Compressive Sensing MRI , 2019, IEEE Transactions on Medical Imaging.

[49]  P. Mansfield,et al.  Medical imaging by NMR. , 1977, The British journal of radiology.

[50]  Xiao Han,et al.  MR‐based synthetic CT generation using a deep convolutional neural network method , 2017, Medical physics.

[51]  S. Majumdar,et al.  Use of 2D U-Net Convolutional Neural Networks for Automated Cartilage and Meniscus Segmentation of Knee MR Imaging Data to Determine Relaxometry and Morphometry. , 2018, Radiology.

[52]  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.

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

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

[55]  A. McMillan,et al.  Deep learning Mr imaging–based attenuation correction for PeT/Mr imaging 1 , 2017 .

[56]  J. Pauly,et al.  Deep learning enables reduced gadolinium dose for contrast‐enhanced brain MRI , 2018, Journal of magnetic resonance imaging : JMRI.

[57]  J. Duyn,et al.  Magnetic susceptibility mapping of brain tissue in vivo using MRI phase data , 2009, Magnetic resonance in medicine.

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

[59]  Lau Pc,et al.  Image formation by induced local interactions. Examples employing nuclear magnetic resonance. 1973. , 1989 .

[60]  Jaejun Yoo,et al.  Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks , 2018, IEEE Transactions on Biomedical Engineering.

[61]  Christopher Joseph Pal,et al.  Brain tumor segmentation with Deep Neural Networks , 2015, Medical Image Anal..

[62]  Michael Unser,et al.  Deep Convolutional Neural Network for Inverse Problems in Imaging , 2016, IEEE Transactions on Image Processing.

[63]  D. Rueckert,et al.  White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks , 2017, NeuroImage: Clinical.

[64]  S. Choi,et al.  Improving Arterial Spin Labeling by Using Deep Learning. , 2017, Radiology.

[65]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[66]  Lei Zhang,et al.  Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.

[67]  Qian Wang,et al.  Deep embedding convolutional neural network for synthesizing CT image from T1‐Weighted MR image , 2018, Medical Image Anal..

[68]  Jin Keun Seo,et al.  Deep learning for undersampled MRI reconstruction , 2017, Physics in medicine and biology.

[69]  Tammy Riklin-Raviv,et al.  Ensemble of expert deep neural networks for spatio‐temporal denoising of contrast‐enhanced MRI sequences , 2017, Medical Image Anal..

[70]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[71]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[72]  Daniel Cremers,et al.  q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans , 2016, IEEE Transactions on Medical Imaging.

[73]  Sascha Krueger,et al.  Prospective real‐time correction for arbitrary head motion using active markers , 2009, Magnetic resonance in medicine.

[74]  Sung Soo Ahn,et al.  Deep-learned 3D black-blood imaging using automatic labelling technique and 3D convolutional neural networks for detecting metastatic brain tumors , 2018, Scientific Reports.