🔥🔥 This is a collection of awesome articles about deep learning in MRI reconstruction🔥🔥
- Our survey paper on arXiv: [Deep learning-based compressed sensing magnetic resonance imaging: a systematic revie](coming soon) ❤️
@article{safari2024fast,
title={Fast MRI Reconstruction Using Deep Learning-based Compressed Sensing: A Systematic Review},
author={Mojtaba Safari and Zach Eidex and Chih-Wei Chang and Richard L. J. Qiu and Xiaofeng Yang},
year={2024},
eprint={2405.00241},
archivePrefix={arXiv},
primaryClass={physics.med-ph}
}- First release: April 14, 2024
- FastMRI Reconstruction examples (MATLAB)
- MRI acquisition & image reconstruction
- MRI Reconstruction Using GRAPPA
- Unrolled network on complex-valued data with complex-valued
- CMR Deep learning reconstruction
- Dynamic MRI Reconstruction
- Deep dive into non-Cartesian reconstruction with BART
- Deep dive into 3D Cartesian reconstruction with BART
NPB-REC: A non-parametric Bayesian deep-learning approach for undersampled MRI reconstruction with uncertainty estimation
Khawaled, Samah, and Moti Freiman
[05 February 2024] [Magn. Reson. in Med.]
[Paper] [Github]
High-Frequency Space Diffusion Models for Accelerated MRI
Cao, Chentao, Zhuo-Xu Cui, Yue Wang, Shaonan Liu, Taijin Chen, Hairong Zheng, Dong Liang, and Yanjie Zhu
[09 January 2024] [TMI]
[Paper] [Github]
MA-RECON: Mask-aware deep-neural-network for robust fast MRI k-space interpolation
Avidan, Nitzan, and Moti Freiman
[29 November 2023] [Comput. Methods Programs Biomed. Update.]
[Paper] [Github]
High-resolution spiral real-time cardiac cine imaging with deep learning-based rapid image reconstruction and quantification
Wang, Junyu, Marina Awad, Ruixi Zhou, Zhixing Wang, Xitong Wang, Xue Feng, Yang Yang, Craig Meyer, Christopher M. Kramer, and Michael Salerno
[05 November 2023] [MICCAI]
[Paper]
Highly-accelerated CEST MRI using frequency-offset-dependent k-space sampling and deep-learning reconstruction
Xu, Jianping, Tao Zu, Yi‐Cheng Hsu, Xiaoli Wang, Kannie WY Chan, and Yi Zhang
[22 October 2023] [Magn. Reson. in Med.]
[Paper] [Github]
A Deep Learning-Based Integrated Framework for Quality-Aware Undersampled Cine Cardiac MRI Reconstruction and Analysis
Machado, Inês and Puyol-Antón, Esther and Hammernik, Kerstin and Cruz, Gastão and Ugurlu, Devran and Olakorede, Ihsane and Oksuz, Ilkay and Ruijsink, Bram and Castelo-Branco, Miguel and Young, Alistair and Prieto, Claudia and Schnabel, Julia and King, Andrew
[02 October 2023] [TMI]
[Paper]
Learning ADC maps from accelerated radial k-space diffusion-weighted MRI in mice using a deep CNN-transformer model
Li, Yuemeng, Miguel Romanello Joaquim, Stephen Pickup, Hee Kwon Song, Rong Zhou, and Yong Fan
[20 August 2023] [Magn. Reson. in Med.]
[Paper] [Github]
Noise2Recon: Enabling SNR-robust MRI reconstruction with semi-supervised and self-supervised learning
Desai, Arjun D., Batu M. Ozturkler, Christopher M. Sandino, Robert Boutin, Marc Willis, Shreyas Vasanawala, Brian A. Hargreaves, Christopher Ré, John M. Pauly, and Akshay S. Chaudhari
[10 July 2023] [Magn. Reson. in Med.]
[Paper] [Github]
Accelerated Cardiac MRI Cine with Use of Resolution Enhancement Generative Adversarial Inline Neural Network
Siyeop Yoon, Shiro Nakamori, Amine Amyar, Salah Assana, Julia Cirillo, Manuel A. Morales, Kelvin Chow, Xiaoming Bi, Patrick Pierce, Beth Goddu, Jennifer Rodriguez, Long H. Ngo, Warren J. Manning, and Reza Nezafat
[30 May 2023] [Radiology]
[Paper]
Dual-domain accelerated MRI reconstruction using transformers with learning-based undersampling
Hong, Guan Qiu, Yuan Tao Wei, William AW Morley, Matthew Wan, Alexander J. Mertens, Yang Su, and Hai-Ling Margaret Cheng
[23 February 2023] [CMIG]
[Paper]
Hierarchical Perception Adversarial Learning Framework for Compressed Sensing MRI
Z. Gao, Y. Guo, J. Zhang, T. Zeng and G. Yang
[30 January 2023] [TMI]
[Paper]
Accelerated cardiac diffusion tensor imaging using deep neural network
Liu, Shaonan, Yuanyuan Liu, Xi Xu, Rui Chen, Dong Liang, Qiyu Jin, Hui Liu, Guoqing Chen, and Yanjie Zhu
[05 January 2023] [PMB]
[Paper]
SwinGAN: A dual-domain Swin Transformer-based generative adversarial network for MRI reconstruction
Zhao, Xiang, Tiejun Yang, Bingjie Li, and Xin Zhang
[31 December 2022] [Computers in Biology and Medicine]
[Paper] [Github]
Improving accelerated MRI by deep learning with sparsified complex data
Jin, Zhaoyang, and Qing‐San Xiang
[08 December 2022] [Magn. Reson. in Med.]
[Paper]
SuperMAP: Deep ultrafast MR relaxometry with joint spatiotemporal undersampling
Li, Hongyu, Mingrui Yang, Jee Hun Kim, Chaoyi Zhang, Ruiying Liu, Peizhou Huang, Dong Liang, Xiaoliang Zhang, Xiaojuan Li, and Leslie Ying
[21 September 2022] [Magn. Reson. Med.]
[Paper]
Signal intensity informed multi-coil encoding operator for physics-guided deep learning reconstruction of highly accelerated myocardial perfusion CMR
Demirel, Omer Burak, Burhaneddin Yaman, Chetan Shenoy, Steen Moeller, Sebastian Weingärtner, and Mehmet Akçakaya
[21 September 2022] [Magn. Reson. in Med.]
[Paper] [Github]
Deep learning for fast low-field MRI acquisitions
Ayde, Reina, Tobias Senft, Najat Salameh, and Mathieu Sarracanie
[06 July 2022] [Sci. Rep.]
[Paper]
DBGAN: A dual-branch generative adversarial network for undersampled MRI reconstruction
Liu, Xianzhe, Hongwei Du, Jinzhang Xu, and Bensheng Qiu
[24 March 2022] [Magn. Reson. Imaging]
[Paper]
Adaptive convolutional neural networks for accelerating magnetic resonance imaging via k-space data interpolation
Du, Tianming, Honggang Zhang, Yuemeng Li, Stephen Pickup, Mark Rosen, Rong Zhou, Hee Kwon Song, and Yong Fan
[16 May 2021] [Med. Image Anal.]
[Paper] [Github]
Magnetic resonance parameter mapping using model-guided self-supervised deep learning
Liu, Fang, Richard Kijowski, Georges El Fakhri, and Li Feng
[19 January 2021] [Magn. Reson. in Med.]
[Paper]
Reconstruction of multicontrast MR images through deep learning
Do, Won‐Joon, Sunghun Seo, Yoseob Han, Jong Chul Ye, Seung Hong Choi, and Sung‐Hong Park
[28 January 2020] [Med. Phys.]
[Paper]
k-Space Deep Learning for Accelerated MRI
Han, Yoseo, Leonard Sunwoo, and Jong Chul Ye
[05 July 2019] [TMI]
[Paper] [Github]
Deep learning for undersampled MRI reconstruction
Hyun, Chang Min, Hwa Pyung Kim, Sung Min Lee, Sungchul Lee, and Jin Keun Seo
[25 June 2018] [PMB]
[Paper]
Ultra-Fast T2-Weighted MR Reconstruction Using Complementary T1-Weighted Information
Xiang, Lei, Yong Chen, Weitang Chang, Yiqiang Zhan, Weili Lin, Qian Wang, and Dinggang Shen
[26 September 2018] [MICCAI]
[Paper]
Accelerating T2 mapping of the brain by integrating deep learning priors with low-rank and sparse modeling
Meng, Ziyu, Rong Guo, Yudu Li, Yue Guan, Tianyao Wang, Yibo Zhao, Brad Sutton, Yao Li, and Zhi‐Pei Liang
[29 September 2020] [Magn. Reson. in Med.]
[Paper]
Score-based Generative Priors Guided Model-driven Network for MRI Reconstruction
Xiaoyu Qiao, Weisheng Li, Yuping Huang and Lijian Yang
[5 May 2024] [preprint arXiv]
[Paper]
A Collaborative Model-driven Network for MRI Reconstruction
Xiaoyu Qiao, Weisheng Lia, Guofen Wan, and Yuping Huang
[5 May 2024] [preprint arXiv]
[Paper]
Feasibility of Artificial Intelligence Constrained Compressed SENSE Accelerated 3D Isotropic T1 VISTA Sequence For Vessel Wall MR Imaging: Exploring the Potential of Higher Acceleration Factors Compared to Traditional Compressed SENSE
Yue Ma, Mengmeng Wang, Yuting Qiao, Yafei Wen, Yi Zhu, Ke Jiang, Jianxiu Lian, Dan Tong
[24 April 2024] [Academic Radiology]
[Paper]
A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging
Wang, Zi, Haoming Fang, Chen Qian, Boxuan Shi, Lijun Bao, Liuhong Zhu, Jianjun Zhou, and Xiaobo Qu
[05 February 2024] [J-BHI]
[Paper]
Predictive uncertainty in deep learning–based MR image reconstruction using deep ensembles: Evaluation on the fastMRI data set
Thomas Küstner, Kerstin Hammernik, Daniel Rueckert, Tobias Hepp, Sergios Gatidis
[28 January 2024] [Magn. Reson. in Med.]
[Paper] [Github]
On retrospective k-space subsampling schemes for deep MRI reconstruction
George Yiasemis, Clara I. Sánchez, Jan-Jakob Sonke, and Jonas Teuwen
[04 January 2024] [MRI]
[Paper]
Radial magnetic resonance image reconstruction with a deep unrolled projected fast iterative soft-thresholding network
*Biao Qu, Jialue Zhang, Taishan Kang, Jianzhong Lin, Meijin Lin, Huajun She, Qingxia Wu, Meiyun Wang, and Gaofeng Zheng *
[19 November 2023] [Computers in Biology and Medicine]
[Paper]
Accelerating CEST imaging using a model-based deep neural network with synthetic training data
Xu, Jianping, Tao Zu, Yi‐Cheng Hsu, Xiaoli Wang, Kannie WY Chan, and Yi Zhang
[22 October 2023] [Magn. Reson. in Med.]
[Paper] [Github]
Joint Cross-Attention Network With Deep Modality Prior for Fast MRI Reconstruction
Kaicong Sun, Qian Wang, and Dinggang Shen
[11 September 2023] [TMI]
[Paper] [Github]
Adapting model-based deep learning to multiple acquisition conditions: Ada-MoDL
Aniket Pramanik, Sampada Bhave, Saurav Sajib, Samir D. Sharma, and Mathews Jacob
[18 June 2023] [Magn. Reson. in Med.]
[Paper]
MEDL-Net: A model-based neural network for MRI reconstruction with enhanced deep learned regularizers
Xiaoyu Qiao, Yuping Huang, and Weisheng Li
[19 January 2023] [Magn. Reson. in Med.]
[Paper] [Github]
Deep Learning Reconstruction Enables Prospectively Accelerated Clinical Knee MRI
Patricia M. Johnson, Dana J. Lin, Jure Zbontar, C. Lawrence Zitnick, Anuroop Sriram, Matthew Muckley, James S. Babb, Mitchell Kline, Gina Ciavarra, Erin Alaia, Mohammad Samim, William R. Walter, Liz Calderon, Thomas Pock, Daniel K. Sodickson, Michael P. Recht, and Florian Knoll
[17 January 2023] [Radiology]
[Paper] [Github]
A Joint Group Sparsity-based deep learning for multi-contrast MRI reconstruction
Guo, Di, Gushan Zeng, Hao Fu, Zi Wang, Yonggui Yang, and Xiaobo Qu
[08 December 2022] [J. Magn. Reson.]
[Paper]
An untrained deep learning method for reconstructing dynamic MR images from accelerated model-based data
Kalina P. Slavkova, Julie C. DiCarlo, Viraj Wadhwa, Sidharth Kumar, Chengyue Wu, John Virostko, Thomas E. Yankeelov, and Jonathan I. Tamir
[05 December 2022] [Magn. Reson. in Med.]
[Paper] [Github]
Highly accelerated MR parametric mapping by undersampling the k-space and reducing the contrast number simultaneously with deep learning
Liu, Shaonan, Haoxiang Li, Yuanyuan Liu, Guanxun Cheng, Gang Yang, Haifeng Wang, Hairong Zheng, Dong Liang, and Yanjie Zhu
[08 September 2022] [PMB]
[Paper]
One-Dimensional Deep Low-Rank and Sparse Network for Accelerated MRI
Zi Wang, Chen Qian, Di Guo, Hongwei Sun, Rushuai Li, Bo Zhao, and Xiaobo Qu
[31 August 2022] [TMI]
[Paper] [Online Tool]
A cascade of preconditioned conjugate gradient networks for accelerated magnetic resonance imaging
Moogyeong Kim and Wonzoo Chung
[29 August 2022] [Computer Methods and Programs in Biomedicine]
[Paper]
Simultaneously optimizing sampling pattern for joint acceleration of multi-contrast MRI using model-based deep learning
Sunghun Seo, Huan Minh Luu, Seung Hong Choi, and Sung-Hong Park
[09 June 2022] [Med. Phys.]
[Paper]
High fidelity deep learning-based MRI reconstruction with Instance-wise discriminative feature matching loss
Ke Wang, Jonathan I. Tamir, Alfredo De Goyeneche, Uri Wollner, Rafi Brada, Stella X. Yu, and Michael Lustig
[03 April 2022] [Magn. Reson. in Med.]
[Paper] [Github]
Accelerating susceptibility-weighted imaging with deep learning by complex-valued convolutional neural network (ComplexNet): validation in clinical brain imaging.
Caohui Duan, Yongqin Xiong, Kun Cheng, Sa Xiao, Jinhao Lyu, Cheng Wang, Xiangbing Bian, Jing Zhang, Dekang Zhang, Ling Chen, Xin Zhou, and Xin Lou
[19 February 2022] [European Radiology]
[Paper]
Learning Data Consistency and its Application to Dynamic MR Imaging
Jing Cheng, Zhuo-Xu Cui, Wenqi Huang, Ziwen Ke, Leslie Ying, Haifeng Wang, Yanjie Zhu, and Dong Liang
[12 July 2021] [Magn. Reson. in Med.]
[Paper]
Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity-weighted coil combination
Hammernik, Kerstin, Jo Schlemper, Chen Qin, Jinming Duan, Ronald M. Summers, and Daniel Rueckert
[10 June 2021] [Magn. Reson. in Med.]
[Paper] [Github]
Accelerated multicontrast reconstruction for synthetic MRI using joint parallel imaging and variable splitting networks
Kanghyun Ryu, Jae-Hun Lee, Yoonho Nam, Sung-Min Gho, Ho-Sung Kim, and Dong-Hyun Kim
[18 March 2021] [Med. Phys.]
[Paper] [Github]
On the regularization of feature fusion and mapping for fast MR Multi-contrast imaging via iterative networks
Xinwen Liu, Jing Wang, Hongfu Sun, Shekhar S. Chandra, Stuart Crozier, and Feng Liu
[02 January 2021] [MRI]
[Paper]
RUN-UP: Accelerated multishot diffusion-weighted MRI reconstruction using
Liu, Risheng, Yuxi Zhang, Shichao Cheng, Zhongxuan Luo, and Xin Fan
[11 August 2020] [Magn. Reson. in Med.]
[Paper]
A Deep Framework Assembling Principled Modules for CS-MRI: Unrolling Perspective, Convergence Behaviors, and Practical Modeling
Liu, Risheng, Yuxi Zhang, Shichao Cheng, Zhongxuan Luo, and Xin Fan
[04 August 2020] [TMI]
[Paper]
Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction
Sandino, Christopher M., Peng Lai, Shreyas S. Vasanawala, and Joseph Y. Cheng
[22 July 2020] [Magn. Reson. in Med.]
[Paper]
Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data
Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Steen Moeller, Jutta Ellermann, Kâmil Uğurbil, and Mehmet Akçakaya
[02 July 2020] [Magn. Reson. in Med.]
[Paper] [Github]
A feature-based convolutional neural network for reconstruction of interventional MRI
Blanca Zufiria, Suhao Qiu, Kang Yan, Ruiyang Zhao, Runke Wang, Huajun She, Chengcheng Zhang, Bomin Sun, Pawel Herman, Yiping Du, and Yuan Feng
[19 December 2019] [NMR Biomed.]
[Paper]
A Deep Information Sharing Network for Multi-Contrast Compressed Sensing MRI Reconstruction
Liyan Sun, Zhiwen Fan, Xueyang Fu, Yue Huang, Xinghao Ding, and John Paisley
[09 July 2019] [Transactions on Image Processing]
[Paper]
Parallel imaging and convolutional neural network combined fast MR image reconstruction: Applications in low-latency accelerated real-time imaging
Ziwu Zhou, Fei Han, Vahid Ghodrati, Yu Gao, Wotao Yin, Yingli Yang, and Peng Hu
[28 May 2019] [Med. Phys.]
[Paper]
MLMFNet: A multi-level modality fusion network for multi-modal accelerated MRI reconstruction
Xiuyun Zhou, Zhenxi Zhang, Hongwei Du, Bensheng Qiu
[24 April 2024] [MRI]
[Paper]
DIRECTION: Deep cascaded reconstruction residual-based feature modulation network for fast MRI reconstruction
Yong Sun , Xiaohan Liu, Yiming Liu, Ruiqi Jin, and Yanwei Pang
[18 April 2024] [MRI]
[Paper]
Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study
Aditya Rastogi, Gianluca Brugnara, Martha Foltyn-Dumitru, Mustafa Ahmed Mahmutoglu, Chandrakanth J Preetha, Erich Kobler, Irada Pflüger, et al.
[26 February 2024] [The Lancet Oncology]
[Paper]
DCT-net: Dual-domain cross-fusion transformer network for MRI reconstruction
Wang, Bin, Yusheng Lian, Xingchuang Xiong, Han Zhou, Zilong Liu, and Xiaohao Zhou
[17 January 2024] [MRI]
[Paper]
Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI
Li, Zimeng, Sa Xiao, Cheng Wang, Haidong Li, Xiuchao Zhao, Caohui Duan, Qian Zhou et al.
[09 January 2024] [TMI]
[Paper] [Github]
Joint MAPLE: Accelerated joint T1 and T2s mapping with scan-specific Self-supervised networks
Amir Heydari, Abbas Ahmadi, Tae Hyung Kim, and Berkin Bilgic
[05 January 2024] [Magn. Reson. in Med.]
[Paper] [Github]
IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRI
Ruimin Feng, Qing Wu, Jie Feng, Huajun She, Chunlei Liu, Yuyao Zhang, and Hongjiang Wei
[13 December 2023] [TMI]
[Paper] [Github]
McSTRA: A multi-branch cascaded swin transformer for point spread function-guided robust MRI reconstruction
Mevan Ekanayake, Kamlesh Pawar, Mehrtash Harandi, Gary Egan, and Zhaolin Chen
[13 December 2023] [Computers in Biology and Medicine]
[Paper]
DC-SiamNet: Deep contrastive Siamese network for self-supervised MRI reconstruction
Yanghui Yan, Tiejun Yang, Xiang Zhao, Chunxia Jiao, Aolin Yang, and Jianyu Miao
[28 October 2023] [Computers in Biology and Medicine]
[Paper]
Adaptive diffusion priors for accelerated MRI reconstruction
Alper Güngör, Salman UH Dar, Şaban Öztürk, Yilmaz Korkmaz, Hasan A. Bedel, Gokberk Elmas, Muzaffer Ozbey, and Tolga Çukur
[20 June 2023] [Medical Image Analysis]
[Paper] [Github]
Deep learning based MRI reconstruction with transformer
Zhengliang Wu, Weibin Liao, Chao Yan, Mangsuo Zhao, Guowen Liu, Ning Ma, and Xuesong Li
[01 March 2023] [Computer Methods and Programs in Biomedicine]
[Paper] [Github]
Deep compressed sensing MRI via a gradient-enhanced fusion model
Yuxiang Dai, Chengyan Wang, and He Wang
[25 January 2023] [Med. Phys.]
[Paper]
A Conditional Normalizing Flow for Accelerated Multi-Coil MR Imaging
Jeffrey Wen, Rizwan Ahmad, and Philip Schniter
[02 June 2023] [ICML]
[Paper] [Github]
Accelerated respiratory-resolved 4D-MRI with separable spatio-temporal neural networks
Maarten L. Terpstra, Matteo Maspero, Joost J. C. Verhoeff, and Cornelis A. T. van den Berg
[01 August 2023] [Med. Phys.]
[Paper] [Github]
Deep supervised dictionary learning by algorithm unrolling-Application to fast 2D dynamic MR image reconstruction
Andreas Kofler, Marie-Christine Pali, Tobias Schaeffter, and Christoph Kolbitsch
[24 December 2022] [Med. Phys.]
[Paper] [Github]
Parallel non-Cartesian spatial-temporal dictionary learning neural networks (stDLNN) for accelerating 4D-MRI
Zhijun Wang, Huajun She, Yufei Zhang, and Yiping P. Du
[24 November 2022] [Medical Image Analysis]
[Paper] [Github]
Exploring the Acceleration Limits of Deep Learning Variational Network-based Two-dimensional Brain MRI
Alireza Radmanesh, Matthew J. Muckley , Tullie Murrell, Emma Lindsey, Anuroop Sriram, Florian Knoll, Daniel K. Sodickson, and Yvonne W. Lui
[02 November 2022] [Radiology]
[Paper]
DSMENet: Detail and Structure Mutually Enhancing Network for under-sampled MRI reconstruction
Wang, Yueze, Yanwei Pang, and Chuan Tong
[13 October 2022] [Computers in Biology and Medicine]
[Paper]
Assessment of data consistency through cascades of independently recurrent inference machines for fast and robust accelerated MRI reconstruction
D Karkalousos, S Noteboom, H E Hulst, F M Vos, and M W A Caan
[08 June 2022] [PMB]
[Paper] [Github]
Pyramid Convolutional RNN for MRI Image Reconstruction
Eric Z. Chen, Puyang Wang, Xiao Chen, Terrence Chen, and Shanhui Sun
[22 February 2022] [TMI]
[Paper] [Github]
Complementary time-frequency domain networks for dynamic parallel MR image reconstruction
Chen Qin, Jinming Duan, Kerstin Hammernik, Jo Schlemper, Thomas Küstner, René Botnar, Claudia Prieto, Anthony N. Price, Joseph V. Hajnal, and Daniel Rueckert
[13 July 2021] [Magn. Reson. in Med.]
[Paper] [Github]
A deep cascade of ensemble of dual domain networks with gradient-based T1 assistance and perceptual refinement for fast MRI reconstruction
Balamurali Murugesan, Sriprabha Ramanarayanan, Sricharan Vijayarangan, Keerthi Ram, Naranamangalam R Jagannathan, and Mohanasankar Sivaprakasam
[24 May 2021] [CMIG]
[Paper]
Accelerating quantitative MR imaging with the incorporation of B1 compensation using deep learning
Yan Wu, Yajun Ma, Jiang Du, and Lei Xing
[29 June 2020] [MRI]
[Paper]
DeepcomplexMRI: Exploiting deep residual network for fast Parallel MR imaging with complex convolution
Shanshan Wang, Huitao Cheng, Leslie Ying, Taohui Xiao, Ziwen Ke, Hairong Zheng, and Dong Liang
[08 February 2020] [MRI]
[Paper]
Highly undersampled magnetic resonance imaging reconstruction using autoencoding priors
Qiegen Liu, Qingxin Yang, Huitao Cheng, Shanshan Wang, Minghui Zhang, and Dong Liang
[20 August 2019] [Magn. Reson. in Med.]
[Paper]
Self-Attention Convolutional Neural Network for Improved MR Image Reconstruction
Yan Wu, Yajun Ma, Jing Liu, Jiang Du, and Lei Xing
[01 April 2019] [Information Science]
[Paper]
A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction
Jo Schlemper, Jose Caballero, Joseph V. Hajnal, Anthony N. Price, and Daniel Rueckert
[13 October 2017] [TMI]
[Paper] [Github]
Joint MAPLE: Accelerated joint T1 and T2s mapping with scan-specific Self-supervised networks
Amir Heydari, Abbas Ahmadi, Tae Hyung Kim, and Berkin Bilgic
[05 January 2024] [Magn. Reson. in Med.]
[Paper] [Github]
DC-SiamNet: Deep contrastive Siamese network for self-supervised MRI reconstruction
Yanghui Yan, Tiejun Yang, Xiang Zhao, Chunxia Jiao, Aolin Yang, and Jianyu Miao
[28 October 2023] [Computers in Biology and Medicine]
[Paper]
Noise2Recon: Enabling SNR-robust MRI reconstruction with semi-supervised and self-supervised learning
Desai, Arjun D., Batu M. Ozturkler, Christopher M. Sandino, Robert Boutin, Marc Willis, Shreyas Vasanawala, Brian A. Hargreaves, Christopher Ré, John M. Pauly, and Akshay S. Chaudhari
[10 July 2023] [Magn. Reson. in Med.]
[Paper] [Github]
Dual-domain self-supervised learning for accelerated non-Cartesian MRI reconstruction
Bo Zhou, Jo Schlemper, Neel Dey, Seyed Sadegh Mohseni Salehi, Kevin Sheth, Chi Liu, James S. Duncan, and Michal Sofka
[18 July 2022] [Medical Image Analysis]
[Paper] [Github]
Magnetic resonance parameter mapping using model-guided self-supervised deep learning
Liu, Fang, Richard Kijowski, Georges El Fakhri, and Li Feng
[19 January 2021] [Magn. Reson. in Med.]
[Paper]
Adaptive channel-modulated personalized federated learning for magnetic resonance image reconstruction
Lyu, Jun, Yapeng Tian, Qing Cai, Chengyan Wang, and Jing Qin
[16 August 2023] [Computers in Biology and Medicine]
[Paper] [GitHub]
Federated End-to-End Unrolled Models for Magnetic Resonance Image Reconstruction
Levac, Brett R., Marius Arvinte, and Jonathan I. Tamir
[16 March 2023] [Bioengineering]
[Paper] [GitHub]
Federated Learning of Generative Image Priors for MRI Reconstruction
Elmas, Gokberk, Salman UH Dar, Yilmaz Korkmaz, Emir Ceyani, Burak Susam, Muzaffer Ozbey, Salman Avestimehr, and Tolga Çukur
[09 November 2022] [TMI]
[Paper] [Github]
Specificity-Preserving Federated Learning for MR Image Reconstruction
Feng, Chun-Mei, Yunlu Yan, Shanshan Wang, Yong Xu, Ling Shao, and Huazhu Fu
[26 August 2022] [TMI]
[Paper] [Github]
Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning
Guo, Pengfei, Puyang Wang, Jinyuan Zhou, Shanshan Jiang, and Vishal M. Patel
[10 March 2021] [CVPR]
[Paper] [Github]
Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data
Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Steen Moeller, Jutta Ellermann, Kâmil Uğurbil, and Mehmet Akçakaya
[02 July 2020] [Magn. Reson. in Med.]
[Paper] [Github]
Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology at NYU School of Medicine and NYU Langone Health
Multi-coil raw k-space data [Dataset] [Github]
- Imaging region
- Brain
- Knee
- Prostate
Imperial College London
Single-coil magnitude image data [Dataset]
- Imaging region
- Brain
multi-institutional
Single-coil magnitude image data [Dataset]
- Imaging region
- Brain
Single-coil magnitude image data [Dataset]
- Imaging region
- Cardiac
Multi-coil raw k-space data [Dataset]
- Imaging region
- Cardiovascular
Multi-coil raw k-space data [Dataset]
- Imaging region
- Knee