🔥🔥 This is a collection of awesome articles about deep learning in MRI reconstruction🔥🔥
- Our survey paper on arXiv: Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration ❤️
@article{SAFARI2026108291,
title = {Advancing MRI reconstruction: A systematic review of deep learning and compressed sensing integration},
journal = {Biomedical Signal Processing and Control},
volume = {111},
pages = {108291},
year = {2026},
issn = {1746-8094},
doi = {https://doi.org/10.1016/j.bspc.2025.108291},
url = {https://www.sciencedirect.com/science/article/pii/S174680942500802X},
author = {Mojtaba Safari and Zach Eidex and Chih-Wei Chang and Richard L.J. Qiu and Xiaofeng Yang},
keywords = {Compressed sensing, Magnetic resonance imaging, MRI reconstruction, FastMRI, MRI acceleration, Deep MRI reconstruction, Accelerated MRI reconstruction},
}
arXiv
@article{safari2024fast,
title={Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration},
author={Mojtaba Safari and Zach Eidex and Chih-Wei Chang and Richard L. J. Qiu and Xiaofeng Yang},
year={2025},
eprint={2501.14158},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2501.14158},
}
- First release: April 14, 2024
- Second release: January 25, 2025
- Third release: September 21, 2026
- 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
Acceleration-dependent variability in simultaneous T1/T2 mapping with a hybrid (mixed) sequence: a phantom comparison of SENSE, compressed sensing, and deep learning reconstructions
Masayuki Kanamoto, Yuki Matta, Toshiki Tateishi, Hiroyuki Tsushima, and Tetsuya Tsujikawa
[January 2027] [MRI]
[Paper]
Performance of a deep learning algorithm on deep learning reconstruction-accelerated prostate biparametric MRI: A preliminary study
Young Joon Lee, Moon Hyung Choi, Robert Grimm, Heinrich von Busch, and Marcel Dominik Nickel
[December 2026] [European Journal of Radiology]
[Paper]
Ai-assisted compressed sensing with deep learning reconstruction for accelerated rectal MRI: A prospective intra-individual study of image quality and preoperative staging
Yuedi Ma, Dongqiu Shan, Junhui Yuan, Xiaoxian Zhang, Dechang Yuan, Guangguang An, Nannan Zhao, Zhikai Zhang, Yifan Liu, Xuejun Chen, Yue Wu, and Chunmiao Xu
[December 2026] [European Journal of Radiology]
[Paper]
Enabling highly accelerated multi-shot IVIM-DWI with a Data-sharing-guided framework: Joint image reconstruction and biomarker estimation
Chenglang Yuan, Shihui Chen, Liyuan Liang, Xiaorui Xu, Hailin Xiong, Tianbaige Liu, Yi Li, Qiting Wu, Wing Yat Cheung, Edward S. Hui, Qi Dou, and Hing-Chiu Chang
[October 2026] [NeuroImage]
[Paper]
Multi-contrast MRI acceleration via post-reconstruction fusion
Alexander Nazarov, Nahum Kiryati, Dani Roizen, Ariel Kerpel, Chen Hoffmann, Gahl Greenberg, and Arnaldo Mayer
[14 September 2026] [Medical Image Analysis]
[Paper]
HiFi-Mamba: Dual-Stream W-Laplacian Enhanced Mamba for High-Fidelity MRI Reconstruction
Hongli Chen, Pengcheng Fang, Yuxia Chen, Yingxuan Ren, Jing Hao, Fangfang Tang, Xiaohao Cai, Shanshan Shan, and Feng Liu
[2026] [AAAI]
[Paper]
The Challenge of Rendering Abnormalities Accurately Using Deep Learning-Based Reconstruction in Accelerated Brain MRI: A Disproportionate Loss of Clinically Relevant Image Information at Higher Rates of Acceleration
Shengjia Chen, Patricia M. Johnson, and Yvonne W. Lui
[14 September 2026] [Investigative Radiology]
[Paper]
Optimizing Brachial Plexus Diffusion Tensor Imaging: The Impact of Deep Learning Reconstruction and Simultaneous Multislice Acceleration on Single-shot Echo-planar Imaging
Euddeum Shim, Sungwon Ham, Baek Hyun Kim, Woo Young Kang, Chang Ho Kang, Kyung-Sik Ahn, Kyu-Chong Lee, In Young Choi, InSeong Kim, and Hangseok Choi
[02 September 2026] [Investigative Radiology]
[Paper]
A spatiotemporal dependency-aware lightweight CNN-ViT network for 3D MRF with a balanced acceleration strategy
Jintao Wei, Huihui Ye, Bingchen Shao, Longtao Ma, Qiuping Ding, Zihan Zhou, Xiaozhi Cao, Jianhui Zhong, and Hongjian He
[September 2026] [Medical Image Analysis]
[Paper]
DAINet: Dual-alignment implicit network for accelerated multi-contrast MRI reconstruction
Zhijie Wang, Aiping Liu, Jinbao Wei, Gang Yang, Yue Yu, Wei Wei, and Xun Chen
[September 2026] [Biomedical Signal Processing and Control]
[Paper]
Recurrent Mamba for efficient and high-fidelity MRI reconstruction
Zhaoming Hou, Boying Wu, Guoqing Chen, Qiyu Jin, Dong Liang, and Tieyong Zeng
[September 2026] [Expert Systems with Applications]
[Paper]
Completely free-breathing cardiac MRI using deep learning reconstruction reduces sedation and scan time in children
Aditi Gupta, William Long, Sara Young, Brian Blasiole, Nicholas Mouzakis, Alessandro Scotti, Kyoko Fujimoto, Tarek Alsaied, Mary Schiff, Adam Christopher, and Laura Olivieri
[27 August 2026] [Pediatric Radiology]
[Paper]
Robust T 1 ρ , T 2 , and T 2 * mapping via spin-locked MOLED with synthetic data-driven deep learning reconstruction
Weikun Chen, Qing Lin, Taishan Kang, Jian Wu, Yuchen Zheng, Jianfa Chen, Shuhui Cai, and Congbo Cai
[26 August 2026] [PMB]
[Paper]
Multi-Frequency Feature Guided Progressive Divide-and-Conquer Network for Accelerated MRI Reconstruction
Hanshuo Zhu, Xiaozhen Ren, Xiaqiong Fan, Zhipeng Guo, and Tiejun Yang
[04 August 2026] [J. Imaging Inform. Med.]
[Paper]
GCL-RCA-Net: an RCA-RemUNet-enhanced graph-contrastive framework for hybrid data-driven and dual-branch physics-guided parallel MRI reconstruction
Mahian Kabir Joarder and Md Kamrul Hasan
[03 August 2026] [PMB]
[Paper]
Information-Theoretic Optimization for Task-Adapted Compressed Sensing Magnetic Resonance Imaging
Xinyu Peng, Ziyang Zheng, Wenrui Dai, Duoduo Xue, Shaohui Li, Chenglin Li, Junni Zou, and Hongkai Xiong
[August 2026] [IEEE TPAMI]
[Paper]
Seeking Common Ground While Reserving Differences: Multiple Anatomy Collaborative Framework for Undersampled MRI Reconstruction
Jiangpeng Yan, Chenghui Yu, Hanbo Chen, Zhe Xu, Junzhou Huang, Xiu Li, and Jianhua Yao
[August 2026] [J-BHI]
[Paper]
Accelerated Cardiac MRI Using Deep Learning Cine Imaging: Validation in Pediatric Patients both with and without Congenital Heart Disease
Jonathan Kochav, Junjie Ma, Sachin Jambawalikar, Maggie Fung, Patrick Quarterman, Amee Shah, Kanwal Farooqi, Amara Majeed, Simone Jhaveri, Alexandra Channing, Sophia Hsien, Martin Janich, and Michael P. DiLorenzo
[August 2026] [JCMR]
[Paper]
Clinical evaluation of deep learning accelerated 3D magnetic resonance cholangiopancreatography at 1.5 T and 3 T
Ivan Jambor, Ranjodh S. Dhami, Madhangi Parameswaran, Eugene Milshteyn, Ajeetkumar Gaddipati, Mary Thomas, Arnaud Guidon, Ty Cashen, Miraj Rawal, Nabih Nakrour, Azadeh Tabari, Susie Y. Huang, Clare Tempany-Afdhal, Mukesh G. Harisinghani, and Rory L. Cochran
[August 2026] [European Journal of Radiology]
[Paper]
Accelerated 7T 3D DESS MR Neurography of the Cervical Spine Using CS-CAIPIRINHA and Deep Learning Reconstruction
Frederik Abel, Constantin von Deuster, Andreas J. Walch, Dominik Paul, Marcel Dominik Nickel, and Reto Sutter
[24 July 2026] [Investigative Radiology]
[Paper]
Training Deep Learning Based Dynamic MR Image Reconstruction Using Synthetic Fractals
Anirudh Raman, Olivier Jaubert, Mark Wrobel, Tina Yao, Ruaraidh Campbell, Rebecca R Baker, Ruta Virsinskaite, Daniel Knight, Michael Quail, Jennifer A Steeden, and Vivek Muthurangu
[19 July 2026] [Magn. Reson. in Med.]
[Paper]
Vessel Wall Imaging in 1.5 T MRI Using Deep Learning Reconstruction: Prospective Evaluation of Interchangeability With Standard 3 T MRI
Arwed Elias Michael, Haidara Almansour, Roman Paul, Dominik Nickel, Ralph Strecker, Alexander Brockmann, Vanessa Schoeffling, Sebastian Altmann, and Ahmed E. Othman
[07 July 2026] [Investigative Radiology]
[Paper]
Accelerated Deep-Learning-Based Image Reconstruction for 3D T2 Dark-Fluid in Imaging of Multiple Sclerosis
Sebastian Altmann, Mario A.A. Mercado, Arwed Elias Michael, Mariya Kondova, Roman H. Paul, Marcel Dominik Nickel, Vanessa Ines Schöffling, Vinzenz Fleischer, Marc A. Brockmann, and Ahmed E. Othman
[06 July 2026] [Investigative Radiology]
[Paper]
Clinical Evaluation of A-LIKNet: Deep Learning-accelerated Single-breath-hold CINE Magnetic Resonance Imaging for Cardiac Function Assessment
Jens Kübler, Stanislau Chekan, Siying Xu, Aya Ghoul, Andreas Lingg, Florian Hagen, Jan-Michael Brendel, Konstantin Nikolaou, Thomas Küstner, and Patrick Krumm
[01 July 2026] [Investigative Radiology]
[Paper]
Simultaneous multi-slice Cardiac Diffusion Tensor Imaging with variable CAIPIRINHA shifts and artefact-aware AI
Michael Tänzer, Eun Ji Lim, Huaqi Harvey Qiu, Camila Munoz, Andrew Scott, Dudley Pennell, Pedro Ferreira, Daniel Rueckert, Guang Yang, and Sonia Nielles-Vallespin
[July 2026] [Medical Image Analysis]
[Paper]
End-to-End Co-Optimization of Adaptive k-space Sampling and Reconstruction for Dynamic MRI
George Yiasemis, Jan-Jakob Sonke, and Jonas Teuwen
[2026] [MIDL]
[Paper]
Beyond Diffusion: Consistency Models for One-Step, High-Fidelity MRI Reconstruction
Mary-Brenda Akoda and Chen Qin
[2026] [MIDL]
[Paper]
T1-PILOT: Physics-Informed Learned Optimized Trajectories for T1 Mapping Acceleration
Tamir Shor, Moti Freiman, Chaim Baskin, and Alex Bronstein
[2026] [MIDL]
[Paper]
MASC: Metal-Aware Sampling and Correction via Reinforcement Learning for Accelerated MRI
Zhengyi Lu, Ming Lu, Chongyu Qu, Junchao Zhu, Junlin Guo, Marilyn Lionts, Yanfan Zhu, Yuechen Yang, Tianyuan Yao, Jayasai Rajagopal, Bennett Allan Landman, Xiao Wang, Xinqiang Yan, and Yuankai Huo
[2026] [MIDL]
[Paper]
Rapid multi-parametric quantitative MRI via deep learning-based synthetic-to-real reconstruction and 3D SSFP-MOLED imaging
Jingying Yang, Liuhong Zhu, Kai Xiong, Jianfeng Bao, Qinqin Yang, Weikun Chen, Taishan Kang, Jianjun Zhou, Jianzhong Lin, Liangjie Lin, Zhong Chen, Shuhui Cai, and Congbo Cai
[July 2026] [NeuroImage]
[Paper]
Spatial-temporal and physical constrained deep learning model for simultaneous T1 and T2 reconstruction and mapping (STEP)
Runyu Yang, Haozhong Sun, Xiaoqi Lin, Haokun Li, and Huijun Chen
[July 2026] [QIMS]
[Paper]
Complex-valued Multi-scale Hybrid Attention Network for Fast MRI via Sparsified Data Learning
Yongchun Ma, Yuanzhen Tang, and Zhaoyang Jin
[30 June 2026] [J. Imaging Inform. Med.]
[Paper]
A Deep Dual-Domain Interaction Reconstruction Framework With Adaptive Gating Fusion for Low-Field MRI
Yuan Yang, Sirui Wang, Hanyu Zhang, Yitao Liu, Yuchen Wu, Zhengxiu Wu, Rongsheng Lu, and Xiaowen Jiang
[12 June 2026] [NMR Biomed.]
[Paper]
Reliability of Whole-Liver Liver-Fat-Quantification Between Deep Learning-Accelerated and Standard Volumetric Interpolated Breath-hold Examination Dixon Sequences in a Prospective Oncology Cohort
Stephan Rau, Anna Fink, Ralph Strecker, Marcel Dominik Nickel, Lea Jigme Michel, Vlad Sacalean, Kai Falko Kästingschäfer, David Klemm, Alexander Rau, Fabian Bamberg, Jakob Weiss, and Maximilian Frederik Russe
[June 2026] [Investigative Radiology]
[Paper]
Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models
M. Berk Sahin, Dilek Yalcinkaya, Abolfazl Hashemi, and Behzad Sharif
[2026] [CVPR Workshop]
[Paper] [Github]
FSSCAN: Frequency-Spatial Skip Connection Attention Network for complex-valued MRI reconstruction
Madhu Kashania, Ankit Rajpal, and Aman Gaur
[June 2026] [Biomedical Signal Processing and Control]
[Paper]
Clinical evaluation of accelerated breath-held and free-breathing cine cardiac MRI using model-based deep learning reconstruction (SonicDL) in children and young adults
Murat Kocaoglu, Hieu Ta, Sean M. Lang, Cara E. Morin, and Amol Pednekar
[28 May 2026] [Pediatric Radiology]
[Paper]
Aleatoric uncertainty in accelerated prostate MRI reconstruction: echo-train dropout versus Gaussian noise Monte Carlo sampling
Quintin van Lohuizen, Stefan J. Fransen, Henkjan Huisman, Jelmer M. Wolterink, Thomas C. Kwee, Derya Yakar, and Frank F. J. Simonis
[26 May 2026] [MAGMA]
[Paper]
Knowledge-guided multi-geometric window transformer for cardiac cine MRI reconstruction
Jun Lyu, Guangming Wang, Yunqi Wang, Jing Qin, and Chengyan Wang
[May 2026] [Medical Image Analysis]
[Paper] [Github]
Toward Modality- and Sampling-Universal Learning Strategies for Accelerating Cardiovascular Imaging: Summary of the CMRxRecon2024 Challenge
Fanwen Wang, Zi Wang, Yan Li, Jun Lyu, Chen Qin, Shuo Wang, Kunyuan Guo, Mengting Sun, Mingkai Huang, Haoyu Zhang, Michael Tänzer, Qirong Li, Xinran Chen, Jiahao Huang, Yinzhe Wu, Haosen Zhang, Kian Anvari Hamedani, Yuntong Lyu, Longyu Sun, Qing Li, Tianxing He, Lizhen Lan, Qiong Yao, Ziqiang Xu, Bingyu Xin, Dimitris N. Metaxas, Narges Razizadeh, Shahabedin Nabavi, George Yiasemis, Jonas Teuwen, Zhenxi Zhang, Sha Wang, Chi Zhang, Daniel B. Ennis, Zhihao Xue, Chenxi Hu, Ruru Xu, Ilkay Oksuz, Donghang Lyu, Yanxin Huang, Xinrui Guo, Ruqian Hao, Jaykumar H. Patel, Guanke Cai, Binghua Chen, Yajing Zhang, Sha Hua, Zhensen Chen, Qi Dou, Xiahai Zhuang, Qian Tao, Wenjia Bai, Jing Qin, He Wang, Claudia Prieto, Michael Markl, Alistair Young, Hao Li, Xihong Hu, Lianming Wu, Xiaobo Qu, Guang Yang, and Chengyan Wang
[May 2026] [TMI]
[Paper] [Github]
Adaptive K-Space Radial Sampling for Cardiac MRI with Reinforcement Learning
Ruru Xu and Ilkay Oksuz
[2026] [MICCAI]
[Paper] [Github]
CRUNet-MR-Univ: A Foundation Model for Diverse Cardiac MRI Reconstruction
Donghang Lyu, Marius Staring, Hildo J. Lamb, and Mariya Doneva
[2026] [MICCAI]
[Paper]
Physics-Informed Spatial-Scaling Transformer for Accelerated MRI Reconstruction
Valiyeh Ansarian Nezhad and Tolga Çukur
[2026] [ISBI]
[Paper]
Causality-driven dual-domain network enhanced by Gaussian splatting for magnetic resonance image reconstruction
Tong Hou, Hongqing Zhu, Zhong Zheng, Kai Chen, Ying Wang, and Bingcang Huang
[May 2026] [Neural Networks]
[Paper]
Branch Learning in MRI: More Data, More Models, More Training
Yuyang Li, Yipin Deng, Zijian Zhou, and Peng Hu
[2026] [MICCAI Workshop]
[Paper]
On the Utility of Foundation Models for Fast MRI : Vision-Language-Guided Image Reconstruction
Ruimin Feng, Xingxin He, Ronald Mercer, Zachary Stewart, and Fang Liu
[07 April 2026] [Magn. Reson. in Med.]
[Paper]
Diagnostic assessment of artificial intelligence reconstruction on accelerated prostate MRI: a retrospective, paired, multi-reader multi-case study
Quintin van Lohuizen, Stefan Johannes Fransen, George Yiasemis, Jasper Jonathan Twilt, Christian Roest, Yuki Arita, Jacob Borstlap, Jurgen J. Fütterer, Maarten de Rooij, Dennis B. Rouw, Ivo G. Schoots, Baris Turkbey, Samuel Joseph Withey, Frank F. J. Simonis, Henkjan Huisman, Thomas Christian Kwee, Jonas Teuwen, and Derya Yakar
[02 April 2026] [European Radiology]
[Paper]
Feasibility of Free-breathing Deep Learning-reconstructed Single-Shot Cine MRI in Participants with Arrhythmia: Comparison with Conventional Segmented Cine MRI
Nan Zhang, Fan Du, Caizhong Chen, Xiuzheng Yue, Mengsu Zeng, and Hang Jin
[01 April 2026] [Radiology: Cardiothoracic Imaging]
[Paper]
Evaluating Structural Uncertainty in Accelerated MRI: Are Voxelwise Measures Useful Surrogates?
Luca L. C. Trautmann, Peter A. Wijeratne, Itamar Ronen, and Ivor J. A. Simpson
[2026] [MICCAI]
[Paper]
Image Content Matters: An Image Content Aware State Space Model for Accelerated MRI Reconstruction
Yucong Meng, Zhiwei Yang, Kexue Fu, Zhijian Song, and Yonghong Shi
[2026] [AAAI]
[Paper]
Benchmarking Hybrid CNN -Transformer Versus Pure Transformer Architectures for Accelerated Hyperpolarized 129 Xe MRI Reconstruction
Ramtin Babaeipour, Matthew S. Fox, Grace Parraga, and Alexei Ouriadov
[27 March 2026] [JMRI]
[Paper]
DH-Mamba: Exploring Dual-Domain Hierarchical State Space Models for MRI Reconstruction
Yucong Meng, Zhiwei Yang, Kexue Fu, Zhijian Song, and Yonghong Shi
[March 2026] [IEEE TCSVT]
[Paper] [Github]
Mamba-driven multi-modality MRI reconstruction via scale diversity fusion and frequency purification
Tong Hou, Hongqing Zhu, Zhong Zheng, Kai Chen, Ying Wang, and Bingcang Huang
[March 2026] [Biomedical Signal Processing and Control]
[Paper]
Multi-contrast feature cross entanglement network for joint MR image reconstruction and super-resolution
Guoqing Ge, Weisheng Li, Yucheng Shu, and Xiaoyu Qiao
[March 2026] [Knowledge-Based Systems]
[Paper]
GL-mamba-net: A magnetic resonance imaging restoration network with global-local mamba
Ke Liang, Zhijin Lin, Jie Yang, Teng Yu, Jieru Chi, and Feng Liu
[February 2026] [MRI]
[Paper]
Multi-contrast low-field MRI acceleration with k-space progressive learning and image-space hybrid attention fusion
Xiaohan Xing, Qi Chen, Lequan Yu, Liang Qiu, Lingting Zhu, Lei Xing, and Lianli Liu
[January 2026] [Medical Image Analysis]
[Paper]
Near-Real-Time Multi-Parametric Quantitative MRI using Parallel Non-Cartesian 6D Spatial-Temporal Dictionary Learning Neural Networks
Zhijun Wang, Wenlong Feng, Sifan Wu, Huajun She, Zekang Ding, and Yiping P. Du
[2026] [IEEE TBME]
[Paper]
Scan-Adaptive MRI Undersampling Using Neighbor-Based Optimization (SUNO)
Siddhant Gautam, Angqi Li, Nicole Seiberlich, Jeffrey A. Fessler, and Saiprasad Ravishankar
[2026] [IEEE Transactions on Computational Imaging]
[Paper] [Github]
Deep learning reconstruction for fast cardiovascular magnetic resonance imaging protocol: A comparative study with conventional cardiovascular magnetic resonance
Yiying Hua, Hongfei Lu, Shiya Wang, Xiuzheng Yue, Fan Du, Nan Zhang, Mengmeng Yu, Yinyin Chen, Mengsu Zeng, and Hang Jin
[2026] [JCMR]
[Paper]
Ultrafast deep learning super-resolution single-shot T2-weighted imaging for robust edema visualization in cardiovascular magnetic resonance
Taraneh Aziz-Safaie, Christoph Katemann, Johannes M. Peeters, Oliver M. Weber, Leon M. Bischoff, Dmitrij Kravchenko, Narine Mesropyan, Thomas M. Vollbrecht, Lucia D. Beissel, Claus C. Pieper, Daniel Kuetting, Julian A. Luetkens, and Alexander Isaak
[2026] [JCMR]
[Paper]
PaDIS-MRI: Patch-Based Diffusion for Data-Efficient, Radiologist-Preferred MRI Reconstruction
Rohan Sanda, Asad Aali, Andrew Johnston, Eduardo Pontes Reis, Gordon Wetzstein, and Sara Fridovich-Keil
[2025] [Proceedings of Machine Learning Research ]
[Paper][Github]
Frequency-spatial synergistic network for accelerated multi-contrast MRI reconstruction
Ke Li, Dong Liang, Guoqing Chen, Qiyu Jin, and Tieyong Zeng
[2025] [Computerized Medical Imaging and Graphics]
[Paper]
LMO: Linear Mamba Operator for MRI Reconstruction
Li, Wei and Jiang, Jiawei and Wu, Jie and Yu, Kaihao and Zheng, Jianwei
[2025] [CVPR]
[Paper] [Github]
Conjugate gradient and deep learning reconstructions: reduced time without affecting image quality and nodule detection
Yoshiharu Ohno, Yoshiyuki Ozawa, Takahiro Ueda, Masahiko Nomura, Natsuka Yazawa, Maiko Shinohara, Kaori Yamamoto, Yuichiro Sano, Masato Ikedo, Masanori Ozaki, Masao Yui, Shohei Harada, Saki Takeda, Akiyoshi Iwase, Takeshi Yoshikawa, and Daisuke Takenaka
[24 December 2025] [European Radiology]
[Paper]
Model-based spatiotemporal synthetic data generation framework and deep-learning reconstruction for real-time MRI oxygen extraction fraction mapping
Zejun Wu, Qinqin Yang, Nuowei Ge, Jiechao Wang, Liuhong Zhu, Jianjun Zhou, Jianfeng Bao, Jianfa Chen, Lina Xu, Zhong Chen, Jianhui Zhong, Shuhui Cai, and Congbo Cai
[09 December 2025] [PMB]
[Paper]
Artificial Intelligence-Accelerated vs. Conventional Diffusion-Weighted Imaging for Prostate MRI: Comparing Quality and Quantitative Metrics
Vlad Sacalean, Oliver Gebler, Wei Liu, Ralph Strecker, Elisabeth Weiland, Fabian Bamberg, Jakob Weiß, Maximilian F. Russe, and Hannes Engel
[December 2025] [Academic Radiology]
[Paper]
Accelerated RAKI reconstruction for multi-slice cardiac cine applications
Lucile Quillien, Julien Oster, and Pierre‐André Vuissoz
[19 November 2025] [Med. Phys.]
[Paper]
Magnetic resonance image processing transformer for general accelerated image restoration
Guoyao Shen, Mengyu Li, Stephan Anderson, Chad W. Farris, and Xin Zhang
[17 November 2025] [Sci. Rep.]
[Paper]
DDMamba: A Dual-Domain Mamba for Multi-Modal Magnetic Resonance Imaging Reconstruction With Fourier Fusion
Zhijin Lin, Jie Yang, Teng Yu, Jieru Chi, and Feng Liu
[02 November 2025] [Magn. Reson. in Med.]
[Paper]
Fast and Robust Single-Shot Cine Cardiac MRI Using Deep Learning Super-Resolution Reconstruction
Taraneh Aziz-Safaie, Leon M. Bischoff, Christoph Katemann, Johannes M. Peeters, Dmitrij Kravchenko, Narine Mesropyan, Lucia D. Beissel, Tatjana Dell, Oliver M. Weber, Claus C. Pieper, Daniel Kütting, Julian A. Luetkens, and Alexander Isaak
[October 2025] [Investigative Radiology]
[Paper]
FDGANet: Frequency dynamic graph attention network for undersampled MRI reconstruction
Yushan Xie, Dongping Xiong, Heng Zhao, Hong Zhou, Bingo Wing-Kuen Ling, and Xiaozhi Zhang
[October 2025] [Biomedical Signal Processing and Control]
[Paper]
DGEDDGAN: A dual-domain generator and edge-enhanced dual discriminator generative adversarial network for MRI reconstruction
Qiaohong Liu, Weikun Zhang, Yuting Zhang, Xiaoxiang Han, Yuanjie Lin, Xinyu Li, Keyan Chen
[08 March 2025] [Magnetic Resonance Imaging]
[Paper]
SHFormer: Dynamic spectral filtering convolutional neural network and high-pass kernel generation transformer for adaptive MRI reconstruction
Sriprabha Ramanarayanan, Rahul G.S., Mohammad Al Fahim, Keerthi Ram, Ramesh Venkatesan, Mohanasankar Sivaprakasam
[08 March 2025] [Neural Networks]
[Paper] [Github]
Lightweight Hypercomplex MRI Reconstruction: A Generalized Kronecker-Parameterized Approach
Haosen Zhang, Jiahao Huang, Yinzhe Wu, Congren Dai, Fanwen Wang, Zhenxuan Zhang, and Guang Yang
[02 January 2026] [MLMI, MICCAI Workshop]
[Paper]
Improving Deep Learning for Accelerated MRI With Data Filtering
Kang Lin, Anselm Krainovic, Kun Wang, and Reinhard Heckel
[2025] [NeurIPS]
[Paper]
Towards Prospective Medical Image Reconstruction via Knowledge-Informed Dynamic Optimal Transport
Taoran Zheng, Yan Yang, Xing Li, Xiang Gu, Jian Sun, and Zongben Xu
[2025] [NeurIPS]
[Paper] [Github]
AutoSamp: Autoencoding k-Space Sampling via Variational Information Maximization for 3D MRI
Cagan Alkan, Morteza Mardani, Congyu Liao, Zhitao Li, Shreyas S. Vasanawala, and John M. Pauly
[15 August 2024] [TMI]
[Paper] [Github]
Highly-accelerated CEST MRI using frequency-offset-dependent k-space sampling and deep-learning reconstruction
Chuyu Liu, Zhongsen Li, Zhensen Chen, Benqi Zhao, Zhuozhao Zheng, and Xiaolei Song
[16 April 2024] [Med. Res. in Med.]
[Paper]
Paired conditional generative adversarial network for highly accelerated liver 4D MRI
Di Xu, Xin Miao, Hengjie Liu, Jessica E Scholey, Wensha Yang, Mary Feng, Michael Ohliger, Hui Lin, Yi Lao, and Yang Yang
[17 June 2024] [PMB]
[Paper]
DiffGAN: An adversarial diffusion model with local transformer for MRI reconstruction
Xiang Zhao, Tiejun Yang, Bingjie Li, Aolin Yang, Yanghui Yan, and Chunxia Jiao
[15 March 2024] [MRI]
[Paper]
Deep unfolding network with spatial alignment for multi-modal MRI reconstruction
Hao Zhang, Qi Wang, Jun Shi, Shihui Ying, and Zhijie Wen
[05 February 2024] [Medical Image Analysis]
[Paper]
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]
Domain transformation learning for MR image reconstruction from dual domain input
Changheun Oh, Jun-Young Chung, and Yeji Han
[3 February 2024] [Computers in Biology and Medicine]
[Paper][Github]
FEFA: Frequency Enhanced Multi-Modal MRI Reconstruction With Deep Feature Alignment
Xuanmin Chen, Liyan Ma, Shihui Ying, Dinggang Shen, and Tieyong Zeng
[2024] [JBHI]
[Paper]
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
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[Paper]
Neural Fields for Highly Accelerated 2D Cine Phase Contrast MRI
Pablo Arratia, Martin J. Graves, Mary McLean, Carolin Pirkl, Carola‐Bibiane Schönlieb, Timo Schirmer, Florian Wiesinger, and Matthias J. Ehrhardt
[03 February 2026] [Advanced Science]
[Paper]
Free-breathing dynamic MRI reconstruction via joint time-dependent coil sensitivity estimation using implicit neural representation
Xin Shen, Jie Feng, Zhenghao Li, Qing Zou, Yuyao Zhang, and Hongjiang Wei
[February 2026] [Medical Image Analysis]
[Paper] [Github]
High-Resolution Diffusion-Weighted Imaging With Self-Gated Self-Supervised Unrolled Reconstruction
Zhengguo Tan, Patrick A. Liebig, Annika Hofmann, Frederik B. Laun, and Florian Knoll
[22 January 2026] [Magn. Reson. in Med.]
[Paper]
Unsupervised Dynamic MRI Reconstruction via Low-Rank Patch-Based Tensor Functions with Implicit Neural Representation
Yuanyuan Liu, Yuanbiao Yang, Jing Cheng, Zhuo-Xu Cui, Qingyong Zhu, Congcong Liu, Yuliang Zhu, Jingran Xu, Hairong Zheng, Dong Liang, and Yanjie Zhu
[2026] [J-BHI]
[Paper]
A multi-dynamic low-rank deep image prior for three-dimensional real-time cardiovascular magnetic resonance imaging
Chong Chen, Marc Vornehm, Zhenyu Bu, Preethi Chandrasekaran, Muhammad A. Sultan, Syed M. Arshad, Yingmin Liu, Yuchi Han, and Rizwan Ahmad
[2026] [JCMR]
[Paper]
Implicit Neural Representation-Based MRI Reconstruction Method With Sensitivity Map Constraints
Lixuan Rao, Xinlin Zhang, Yiman Huang, Tao Tan, Huihong Sun, Jixiao Sun, and Tong Tong
[2026] [IEEE TIM]
[Paper]
Cardiac MR Fingerprinting at 0.55T Using a Deep Image Prior for Joint T 1 , T 2 , and M 0 Mapping
Zhongnan Liu, Zexuan Liu, Imran Rashid, Mauricio Stanzione Galizia, Christopher Scoma, William Truesdell, Prachi Agarwal, Nicole Seiberlich, Liyue Shen, and Jesse Hamilton
[22 January 2026] [JMRI]
[Paper]
AeSPa : Attention-guided Self-supervised Parallel Imaging for MRI Reconstruction
Joo, Jinho and Kim, Hyeseong and Won, Hyeyeon and Lee, Deukhee and Eo, Taejoon and Hwang, Dosik
[2025] [CVPR]
[Paper] [Github]
INR Meets Multi-contrast MRI Reconstruction
Natascha Niessen, Carolin M. Pirkl, Ana Beatriz Solana, Hannah Eichhorn, Veronika Spieker, Wenqi Huang, Tim Sprenger, Marion I. Menzel, Julia A. Schnabel, and on behalf of the PREDICTOM consortium
[2025] [MICCAI]
[Paper]
Self-supervised Motion-Compensated Reconstruction for Cardiac Cine MRI
Siying Xu, Aya Ghoul, Kerstin Hammernik, Jens Kuebler, Patrick Krumm, Andreas Lingg, Daniel Rueckert, Sergios Gatidis, and Thomas Küstner
[2025] [MICCAI]
[Paper] [Github]
Accelerating Multiparametric Quantitative MRI Using Self-Supervised Scan-Specific Implicit Neural Representation With Model Reinforcement
Ruimin Feng, Albert Jang, Xingxin He, and Fang Liu
[19 December 2025] [Magn. Reson. in Med.]
[Paper]
Self-Supervised Joint Reconstruction and Denoising of T2 -Weighted PROPELLER MRI of the Lung at 0.55T
Jingjia Chen, Haoyang Pei, Christoph Maier, Mary Bruno, Qiuting Wen, Seon‐Hi Shin, William Moore, Hersh Chandarana, and Li Feng
[12 December 2025] [Magn. Reson. in Med.]
[Paper]
Zero-Shot Dynamic MRI Reconstruction With Global-to-Local Diffusion Model
Yu Guan, Kunlong Zhang, Qi Qi, Dong Wang, Ziwen Ke, Shaoyu Wang, Dong Liang, and Qiegen Liu
[31 August 2025] [NMR Biomed.]
[Paper]
Robust multi-coil MRI reconstruction via self-supervised denoising
Asad Aali, Marius Arvinte, Sidharth Kumar, Yamin I. Arefeen, Jonathan I. Tamir
[02 June 2025] [Magn. Reson. in Med.]
[Paper] [Github]
Fast MRI for All: Bridging Access Gaps by Training without Raw Data
Yasar Utku Alcalar, Merve Gulle, and Mehmet Akcakaya
[2025] [NeurIPS]
[Paper] [Github]
D2SA: Dual-Stage Distribution and Slice Adaptation for Efficient Test-Time Adaptation in MRI Reconstruction
Lipei Zhang, Rui Sun, Zhongying Deng, Yanqi Cheng, Carola-Bibiane Schönlieb, and Angelica Aviles-Rivero
[2025] [NeurIPS]
[Paper]
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]
TTP-SSFL: Test-Time Personalization Self-Supervised Federated Learning for Accelerating MR Image Reconstruction
Chenghu Geng, Mingfeng Jiang, Dongsheng Ruan, Chengjin Yu, Hong Sun, Feng Liu, Ling Xia, and Guang Yang
[2026] [IEEE TNNLS]
[Paper]
Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstruction
Valiyeh A. Nezhad, Gokberk Elmas, Bilal Kabas, Fuat Arslan, Tolga Çukur
[06 February 2025] [preprint arXiv]
[Paper] [Github]
FAME: A Federated Adversarial Learning Framework for Privacy-Preserving MRI Reconstruction
Shahzad Ahmed, Jinchao Feng, Javed Ferzund, Muhammad Yaqub, Muhammad Usman Ali, Malik Abdul Manan, and Abdul Raheem
[25 January 2025] [Appl Magn Reson]
[Paper]
Generalizable Reconstruction for Accelerating MR Imaging via Federated Learning With Neural Architecture Search
Ruoyou Wu, Cheng Li, Juan Zou, Xinfeng Liu, Hairong Zheng, and Shanshan Wang
[22 July 2024] [TMI]
[Paper] [GitHub]
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]
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]
Human Phenome Institute, Fudan University, Shanghai, China
Multi-coil raw k-space data [Dataset] [Github]
- Imaging region
- Cardiac MRI
- T1, T2, T2* maps
- T1w and T2w
- T1ρ
- Black blood
- Phase contrast
- LGE
- Perfusion
- Cardiac MRI
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
- Breast
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
Multi-coil raw k-space data [Dataset]
- Imaging region
- Brain
Multi-coil raw k-space data [Dataset]
- Imaging region
- Knee