Two-Stage Deep Learning for Accelerated 3D Time-of-Flight MRA without Matched Training Data

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

[2]  Frank Ong,et al.  Unsupervised MRI Reconstruction with Generative Adversarial Networks , 2020, ArXiv.

[3]  Joachim Hornegger,et al.  Highly undersampled peripheral Time-of-Flight magnetic resonance angiography: optimized data acquisition and iterative image reconstruction , 2015, Magnetic Resonance Materials in Physics, Biology and Medicine.

[4]  Guanhua Wang,et al.  Accelerated MRI Reconstruction with Dual-Domain Generative Adversarial Network , 2019, MLMIR@MICCAI.

[5]  Andrew J Wheaton,et al.  Non‐contrast enhanced MR angiography: Physical principles , 2012, Journal of magnetic resonance imaging : JMRI.

[6]  Steen Moeller,et al.  Self‐supervised learning of physics‐guided reconstruction neural networks without fully sampled reference data , 2019, Magnetic resonance in medicine.

[7]  Guang Yang,et al.  Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI , 2018, MICCAI.

[8]  F A Jolesz,et al.  Cerebral MR angiography with multiple overlapping thin slab acquisition. Part II. Early clinical experience. , 1992, Radiology.

[9]  Jong Chul Ye,et al.  Optimal Transport Driven CycleGAN for Unsupervised Learning in Inverse Problems , 2019, SIAM J. Imaging Sci..

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

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

[12]  Jong Chul Ye,et al.  Unpaired Training of Deep Learning tMRA for Flexible Spatio-Temporal Resolution , 2020, IEEE Transactions on Medical Imaging.

[13]  M. Lustig,et al.  Improving non‐contrast‐enhanced steady‐state free precession angiography with compressed sensing , 2009, Magnetic resonance in medicine.

[14]  A. Enis Çetin,et al.  Projection onto Epigraph Sets for Rapid Self-Tuning Compressed Sensing MRI , 2018, IEEE Transactions on Medical Imaging.

[15]  Dong Liang,et al.  DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution. , 2020, Magnetic resonance imaging.

[16]  Jong Chul Ye,et al.  Geometric Approaches to Increase the Expressivity of Deep Neural Networks for MR Reconstruction , 2020, IEEE Journal of Selected Topics in Signal Processing.

[17]  P. J. Keller,et al.  MR angiography with two-dimensional acquisition and three-dimensional display. Work in progress. , 1989, Radiology.

[18]  Leslie Ying,et al.  Accelerating magnetic resonance imaging via deep learning , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[19]  J. C. Ye,et al.  Optimal Transport, CycleGAN, and Penalized LS for Unsupervised Learning in Inverse Problems , 2019, ArXiv.

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

[21]  Jing Liu,et al.  Clinical feasibility study of 3D intracranial magnetic resonance angiography using compressed sensing , 2019, Journal of magnetic resonance imaging : JMRI.

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

[23]  Liyuan Liu,et al.  On the Variance of the Adaptive Learning Rate and Beyond , 2019, ICLR.

[24]  Geoffrey E. Hinton,et al.  Lookahead Optimizer: k steps forward, 1 step back , 2019, NeurIPS.

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

[26]  Mohammad Shahdloo,et al.  Prior-Guided Image Reconstruction for Accelerated Multi-Contrast MRI via Generative Adversarial Networks , 2020, IEEE Journal of Selected Topics in Signal Processing.

[27]  Jong Chul Ye,et al.  Unpaired Deep Learning for Accelerated MRI Using Optimal Transport Driven CycleGAN , 2020, IEEE Transactions on Computational Imaging.

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

[29]  Aaron Defazio,et al.  End-to-End Variational Networks for Accelerated MRI Reconstruction , 2020, MICCAI.

[30]  Hehan Tang,et al.  Accelerated Time-of-Flight Magnetic Resonance Angiography with Sparse Undersampling and Iterative Reconstruction for the Evaluation of Intracranial Arteries , 2018, Korean journal of radiology.

[31]  Daniel Rueckert,et al.  A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction , 2017, IEEE Transactions on Medical Imaging.

[32]  Yuichi Yoshida,et al.  Spectral Normalization for Generative Adversarial Networks , 2018, ICLR.

[33]  Michaela Schmidt,et al.  Highly undersampled contrast‐enhanced MRA with iterative reconstruction: Integration in a clinical setting , 2015, Magnetic resonance in medicine.

[34]  Gabriel Peyré,et al.  Computational Optimal Transport , 2018, Found. Trends Mach. Learn..

[35]  T. Çukur,et al.  Progressively Volumetrized Deep Generative Models for Data-Efficient Contextual Learning of MR Image Recovery , 2020, Medical Image Anal..

[36]  Dong Liang,et al.  DIMENSION: Dynamic MR imaging with both k‐space and spatial prior knowledge obtained via multi‐supervised network training , 2018, NMR in biomedicine.

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

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

[39]  D. Feinberg,et al.  Halving MR imaging time by conjugation: demonstration at 3.5 kG. , 1986, Radiology.

[40]  Luca Antiga,et al.  Automatic differentiation in PyTorch , 2017 .

[41]  Alexandros G. Dimakis,et al.  AmbientGAN: Generative models from lossy measurements , 2018, ICLR.

[42]  Song Han,et al.  Deep Generative Adversarial Networks for Compressed Sensing Automates MRI , 2017, ArXiv.

[43]  Dong Liang,et al.  IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRI , 2019, IEEE Transactions on Computational Imaging.

[44]  G. Laub.  Time-of-flight method of MR angiography. , 1995, Magnetic resonance imaging clinics of North America.

[45]  Jong Chul Ye,et al.  A General Framework for Compressed Sensing and Parallel MRI Using Annihilating Filter Based Low-Rank Hankel Matrix , 2015, IEEE Transactions on Computational Imaging.

[46]  Jong Chul Ye,et al.  k‐t FOCUSS: A general compressed sensing framework for high resolution dynamic MRI , 2009, Magnetic resonance in medicine.

[47]  P. Boesiger,et al.  SENSE: Sensitivity encoding for fast MRI , 1999, Magnetic resonance in medicine.

[48]  D L Parker,et al.  Cerebral MR angiography with multiple overlapping thin slab acquisition. Part I. Quantitative analysis of vessel visibility. , 1991, Radiology.

[49]  拓海 杉山,et al.  “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .

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

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

[52]  M. Akahane,et al.  Non‐contrast enhanced MR angiography: Established techniques , 2012, Journal of magnetic resonance imaging : JMRI.

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