@String(PAMI = {IEEE Trans. Pattern Anal. Mach. Intell.})
@String(IJCV = {Int. J. Comput. Vis.})
@String(CVPR= {IEEE Conf. Comput. Vis. Pattern Recog.})
@String(ICCV= {Int. Conf. Comput. Vis.})
@String(ECCV= {Eur. Conf. Comput. Vis.})
@String(NIPS= {Adv. Neural Inform. Process. Syst.})
@String(ICPR = {Int. Conf. Pattern Recog.})
@String(BMVC= {Brit. Mach. Vis. Conf.})
@String(TOG= {ACM Trans. Graph.})
@String(TIP  = {IEEE Trans. Image Process.})
@String(TVCG  = {IEEE Trans. Vis. Comput. Graph.})
@String(TMM  = {IEEE Trans. Multimedia})
@String(ACMMM= {ACM Int. Conf. Multimedia})
@String(ICME = {Int. Conf. Multimedia and Expo})
@String(ICASSP=	{ICASSP})
@String(ICIP = {IEEE Int. Conf. Image Process.})
@String(ACCV  = {ACCV})
@String(ICLR = {Int. Conf. Learn. Represent.})
@String(IJCAI = {IJCAI})
@String(PR   = {Pattern Recognition})
@String(AAAI = {AAAI})
@String(CVPRW= {IEEE Conf. Comput. Vis. Pattern Recog. Worksh.})
@String(CSVT = {IEEE Trans. Circuit Syst. Video Technol.})

@String(SPL	= {IEEE Sign. Process. Letters})
@String(VR   = {Vis. Res.})
@String(JOV	 = {J. Vis.})
@String(TVC  = {The Vis. Comput.})
@String(JCST  = {J. Comput. Sci. Tech.})
@String(CGF  = {Comput. Graph. Forum})
@String(CVM = {Computational Visual Media})


@String(PAMI  = {IEEE TPAMI})
@String(IJCV  = {IJCV})
@String(CVPR  = {CVPR})
@String(ICCV  = {ICCV})
@String(ECCV  = {ECCV})
@String(NIPS  = {NeurIPS})
@String(ICPR  = {ICPR})
@String(BMVC  =	{BMVC})
@String(TOG   = {ACM TOG})
@String(TIP   = {IEEE TIP})
@String(TVCG  = {IEEE TVCG})
@String(TCSVT = {IEEE TCSVT})
@String(TMM   =	{IEEE TMM})
@String(ACMMM = {ACM MM})
@String(ICME  =	{ICME})
@String(ICASSP=	{ICASSP})
@String(ICIP  = {ICIP})
@String(ACCV  = {ACCV})
@String(ICLR  = {ICLR})
@String(IJCAI = {IJCAI})
@String(PR = {PR})
@String(AAAI = {AAAI})
@String(CVPRW= {CVPRW})
@String(CSVT = {IEEE TCSVT})



@article{Yoo2021,
  title = {Time-Dependent Deep Image Prior for Dynamic MRI},
  volume = {40},
  ISSN = {1558-254X},
  DOI = {10.1109/tmi.2021.3084288},
  number = {12},
  journal = {IEEE Transactions on Medical Imaging},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
  author = {Yoo,  Jaejun and Jin,  Kyong Hwan and Gupta,  Harshit and Yerly,  Jerome and Stuber,  Matthias and Unser,  Michael},
  year = {2021},
  month = dec,
  pages = {3337--3348}
}



@article{Qin2019,
  title = {Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction},
  volume = {38},
  ISSN = {1558-254X},
  DOI = {10.1109/tmi.2018.2863670},
  number = {1},
  journal = {IEEE Transactions on Medical Imaging},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
  author = {Qin,  Chen and Schlemper,  Jo and Caballero,  Jose and Price,  Anthony N. and Hajnal,  Joseph V. and Rueckert,  Daniel},
  year = {2019},
  month = jan,
  pages = {280--290}
}

@article{Wang2021,
  title = {Recommendation for Cardiac Magnetic Resonance Imaging-Based Phenotypic Study: Imaging Part},
  volume = {1},
  ISSN = {2730-5848},
  DOI = {10.1007/s43657-021-00018-x},
  number = {4},
  journal = {Phenomics},
  publisher = {Springer Science and Business Media LLC},
  author = {Wang,  Chengyan and Li,  Yan and Lv,  Jun and Jin,  Jianhua and Hu,  Xumei and Kuang,  Xutong and Chen,  Weibo and Wang,  He},
  year = {2021},
  month = jul,
  pages = {151--170}
}

@misc{cmrxrecon2023,
  doi = {10.48550/ARXIV.2309.10836},
  author = {Wang,  Chengyan and others},
  keywords = {Computer Vision and Pattern Recognition (cs.CV),  FOS: Computer and information sciences,  FOS: Computer and information sciences},
  title = {CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction},
  publisher = {arXiv},
  year = {2023},
  copyright = {arXiv.org perpetual,  non-exclusive license}
}

@ARTICLE{1697831,
  author={Shannon, C.E.},
  journal={Proceedings of the IRE}, 
  title={Communication in the Presence of Noise}, 
  year={1949},
  volume={37},
  number={1},
  pages={10-21},
  keywords={Electron tubes;Voltage;Bandwidth;Circuits;Shape;Klystrons;Frequency measurement;Gain measurement;Communication systems;Telephony},
  doi={10.1109/JRPROC.1949.232969}}

@inproceedings{zhang2023radial,
  title={Radial-rim: accelerated radial 4D MRI using the recurrent inference machine},
  author={Zhang, C and Caan, MW and Navest, R and Teuwen, J and Sonke, JJ},
  booktitle={Proceedings of International Society for Magnetic Resonance in Medicine},
  volume={31},
  year={2023}
}

@article{lustig2008compressed,
  title={Compressed sensing MRI},
  author={Lustig, Michael and Donoho, David L and Santos, Juan M and Pauly, John M},
  journal={IEEE signal processing magazine},
  volume={25},
  number={2},
  pages={72--82},
  year={2008},
  publisher={IEEE}
}

@article{peng2022deepsense,
  title={DeepSENSE: Learning coil sensitivity functions for SENSE reconstruction using deep learning},
  author={Peng, Xi and Sutton, Bradley P and Lam, Fan and Liang, Zhi-Pei},
  journal={Magnetic resonance in medicine},
  volume={87},
  number={4},
  pages={1894--1902},
  year={2022},
  publisher={Wiley Online Library}
}

@article{qiao2023medl,
  title={MEDL-Net: A model-based neural network for MRI reconstruction with enhanced deep learned regularizers},
  author={Qiao, Xiaoyu and Huang, Yuping and Li, Weisheng},
  journal={Magnetic Resonance in Medicine},
  volume={89},
  number={5},
  pages={2062--2075},
  year={2023},
  publisher={Wiley Online Library}
}

@inproceedings{sriram2020end,
  title={End-to-end variational networks for accelerated MRI reconstruction},
  author={Sriram, Anuroop and Zbontar, Jure and Murrell, Tullie and Defazio, Aaron and Zitnick, C Lawrence and Yakubova, Nafissa and Knoll, Florian and Johnson, Patricia},
  booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2020: 23rd International Conference, Lima, Peru, October 4--8, 2020, Proceedings, Part II 23},
  pages={64--73},
  year={2020},
  organization={Springer}
}



@inproceedings{Yiasemis_2022_CVPR,
    author    = {Yiasemis, George and Sonke, Jan-Jakob and S\'anchez, Clarisa and Teuwen, Jonas},
    title     = {Recurrent Variational Network: A Deep Learning Inverse Problem Solver Applied to the Task of Accelerated MRI Reconstruction},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {732-741},
    editor = {}
}

@article{lyu2024state,
  title={The state-of-the-art in cardiac mri reconstruction: Results of the cmrxrecon challenge in miccai 2023},
  author={Lyu, Jun and Qin, Chen and Wang, Shuo and Wang, Fanwen and Li, Yan and Wang, Zi and Guo, Kunyuan and Ouyang, Cheng and T{\"a}nzer, Michael and Liu, Meng and others},
  journal={Medical Image Analysis},
  pages={103485},
  year={2025},
  publisher={Elsevier}
}

@article{guo2023reconformer,
  title={ReconFormer: Accelerated MRI reconstruction using recurrent transformer},
  author={Guo, Pengfei and Mei, Yiqun and Zhou, Jinyuan and Jiang, Shanshan and Patel, Vishal M},
  journal={IEEE transactions on medical imaging},
  year={2023},
  publisher={IEEE}
}

@incollection{bertero2006regularization,
  title={Regularization methods for linear inverse problems},
  author={Bertero, M},
  booktitle={Inverse Problems: Lectures given at the 1st 1986 Session of the Centro Internazionale Matematico Estivo (CIME) held at Montecatini Terme, Italy, May 28--June 5, 1986},
  pages={52--112},
  year={2006},
  publisher={Springer}
}

@article{gamper2008compressed,
  title={Compressed sensing in dynamic MRI},
  author={Gamper, Urs and Boesiger, Peter and Kozerke, Sebastian},
  journal={Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine},
  volume={59},
  number={2},
  pages={365--373},
  year={2008},
  publisher={Wiley Online Library}
}

@InProceedings{Yiasemis2024,
author="Yiasemis, George
and Moriakov, Nikita
and Sonke, Jan-Jakob
and Teuwen, Jonas",
editor="Camara, Oscar
and Puyol-Ant{\'o}n, Esther
and Sermesant, Maxime
and Suinesiaputra, Avan
and Tao, Qian
and Wang, Chengyan
and Young, Alistair",
title="Deep Cardiac MRI Reconstruction with ADMM",
booktitle="Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="479--490",
isbn="978-3-031-52448-6",
  DOI = {10.1007/978-3-031-52448-6\_45},
}

@article{bakker2020experimental,
  title={Experimental design for MRI by greedy policy search},
  author={Bakker, Tim and van Hoof, Herke and Welling, Max},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={18954--18966},
  year={2020}
}

@article{xu2015empirical,
  title={Empirical evaluation of rectified activations in convolutional network},
  author={Xu, Bing},
  journal={arXiv preprint arXiv:1505.00853},
  year={2015}
}

@inproceedings{
bakker2022on,
title={On learning adaptive acquisition policies for undersampled multi-coil {MRI} reconstruction},
author={Tim Bakker and Matthew J. Muckley and Adriana Romero-Soriano and Michal Drozdzal and Luis Pineda},
booktitle={Medical Imaging with Deep Learning},
year={2022},
}


@article{yin2019understanding,
  title={Understanding straight-through estimator in training activation quantized neural nets},
  author={Yin, Penghang and Lyu, Jiancheng and Zhang, Shuai and Osher, Stanley and Qi, Yingyong and Xin, Jack},
  journal={arXiv preprint arXiv:1903.05662},
  year={2019}
}

@inbook{Bahadir2019,
  title = {Learning-Based Optimization of the Under-Sampling Pattern in MRI},
  ISBN = {9783030203511},
  ISSN = {1611-3349},
  DOI = {10.1007/978-3-030-20351-1_61},
  booktitle = {Information Processing in Medical Imaging},
  publisher = {Springer International Publishing},
  author = {Bahadir,  Cagla Deniz and Dalca,  Adrian V. and Sabuncu,  Mert R.},
  year = {2019},
  pages = {780--792}
}


@InProceedings{pmlr-v139-van-gorp21a,
  title = 	 {Active Deep Probabilistic Subsampling},
  author =       {Van Gorp, Hans and Huijben, Iris and Veeling, Bastiaan S and Pezzotti, Nicola and Van Sloun, Ruud J. G.},
  booktitle = 	 {Proceedings of the 38th International Conference on Machine Learning},
  pages = 	 {10509--10518},
  year = 	 {2021},
  editor = 	 {Meila, Marina and Zhang, Tong},
  volume = 	 {139},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {18--24 Jul},
  publisher =    {PMLR},
  abstract = 	 {Subsampling a signal of interest can reduce costly data transfer, battery drain, radiation exposure and acquisition time in a wide range of problems. The recently proposed Deep Probabilistic Subsampling (DPS) method effectively integrates subsampling in an end-to-end deep learning model, but learns a static pattern for all datapoints. We generalize DPS to a sequential method that actively picks the next sample based on the information acquired so far; dubbed Active-DPS (A-DPS). We validate that A-DPS improves over DPS for MNIST classification at high subsampling rates. Moreover, we demonstrate strong performance in active acquisition Magnetic Resonance Image (MRI) reconstruction, outperforming DPS and other deep learning methods.}
}


@misc{gautam2024patientadaptive,
      title={Patient-Adaptive and Learned MRI Data Undersampling Using Neighborhood Clustering}, 
      author={Siddhant Gautam and Angqi Li and Saiprasad Ravishankar},
      year={2024},
      eprint={2312.08507},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

@InProceedings{pineda2020,
author="Pineda, Luis
and Basu, Sumana
and Romero, Adriana
and Calandra, Roberto
and Drozdzal, Michal",
editor="Martel, Anne L.
and Abolmaesumi, Purang
and Stoyanov, Danail
and Mateus, Diana
and Zuluaga, Maria A.
and Zhou, S. Kevin
and Racoceanu, Daniel
and Joskowicz, Leo",
title="Active MR k-space Sampling with Reinforcement Learning",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2020",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="23--33",
abstract="Deep learning approaches have recently shown great promise in accelerating magnetic resonance image (MRI) acquisition. The majority of existing work have focused on designing better reconstruction models given a pre-determined acquisition trajectory, ignoring the question of trajectory optimization. In this paper, we focus on learning acquisition trajectories given a fixed image reconstruction model. We formulate the problem as a sequential decision process and propose the use of reinforcement learning to solve it. Experiments on a large scale public MRI dataset of knees show that our proposed models significantly outperform the state-of-the-art in active MRI acquisition, over a large range of acceleration factors.",
isbn="978-3-030-59713-9"
}

@article{kingma2014adam,
  title={Adam: A method for stochastic optimization},
  author={Kingma, Diederik P},
  journal={arXiv preprint arXiv:1412.6980},
  year={2014}
}

@misc{vsharp2023,
title = {vSHARP: Variable Splitting Half-quadratic ADMM algorithm for reconstruction of inverse-problems},
journal = {Magnetic Resonance Imaging},
volume = {115},
pages = {110266},
year = {2025},
issn = {0730-725X},
doi = {https://doi.org/10.1016/j.mri.2024.110266},
url = {https://www.sciencedirect.com/science/article/pii/S0730725X24002479},
author = {George Yiasemis and Nikita Moriakov and Jan-Jakob Sonke and Jonas Teuwen},
}

@article{knoll2020deep,
  title={Deep-learning methods for parallel magnetic resonance imaging reconstruction: A survey of the current approaches, trends, and issues},
  author={Knoll, Florian and Hammernik, Kerstin and Zhang, Chi and Moeller, Steen and Pock, Thomas and Sodickson, Daniel K and Akcakaya, Mehmet},
  journal={IEEE signal processing magazine},
  volume={37},
  number={1},
  pages={128--140},
  year={2020},
  publisher={IEEE}
}

@article{LONNING2024102348,
title = {Dynamic recurrent inference machines for accelerated MRI-guided radiotherapy of the liver},
journal = {Computerized Medical Imaging and Graphics},
pages = {102348},
year = {2024},
issn = {0895-6111},
doi = {https://doi.org/10.1016/j.compmedimag.2024.102348},
author = {Kai L{\o}nning and Matthan W.A. Caan and Marlies E. Nowee and Jan-Jakob Sonke},
}

@inproceedings{
shor2023multi,
title={Multi {PILOT}: Feasible Learned Multiple Acquisition Trajectories For Dynamic {MRI}},
author={Tamir Shor},
booktitle={Medical Imaging with Deep Learning},
year={2023},
}

@INPROCEEDINGS{8099920,
  author={Ulyanov, Dmitry and Vedaldi, Andrea and Lempitsky, Victor},
  booktitle={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={Improved Texture Networks: Maximizing Quality and Diversity in Feed-Forward Stylization and Texture Synthesis}, 
  year={2017},
  volume={},
  number={},
  pages={4105-4113},
  keywords={Generators;Neural networks;Optimization;Gallium nitride;Convolutional codes;Monte Carlo methods;Entropy},
  doi={10.1109/CVPR.2017.437}}


@article{DBLP:journals/corr/BengioLC13,
  author       = {Yoshua Bengio and
                  Nicholas L{\'{e}}onard and
                  Aaron C. Courville},
  title        = {Estimating or Propagating Gradients Through Stochastic Neurons for
                  Conditional Computation},
  journal      = {CoRR},
  year         = {2013},
  eprinttype    = {arXiv},
  eprint       = {1308.3432},
  timestamp    = {Mon, 13 Aug 2018 16:47:35 +0200},
}

@article{kustner2020cinenet,
  title={CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions},
  author={K{\"u}stner, Thomas and Fuin, Niccolo and Hammernik, Kerstin and Bustin, Aurelien and Qi, Haikun and Hajhosseiny, Reza and Masci, Pier Giorgio and Neji, Radhouene and Rueckert, Daniel and Botnar, Ren{\'e} M and others},
  journal={Scientific reports},
  volume={10},
  number={1},
  pages={13710},
  year={2020},
  publisher={Nature Publishing Group UK London}
}

@article{huang2021dynamic,
  title={Dynamic MRI reconstruction with end-to-end motion-guided network},
  author={Huang, Qiaoying and Xian, Yikun and Yang, Dong and Qu, Hui and Yi, Jingru and Wu, Pengxiang and Metaxas, Dimitris N},
  journal={Medical Image Analysis},
  volume={68},
  pages={101901},
  year={2021},
  publisher={Elsevier}
}

@article{zhang2022compressed,
  title={Compressed sensing based dynamic MR image reconstruction by using 3D-total generalized variation and tensor decomposition: kt TGV-TD},
  author={Zhang, Jucheng and Han, Lulu and Sun, Jianzhong and Wang, Zhikang and Xu, Wenlong and Chu, Yonghua and Xia, Ling and Jiang, Mingfeng},
  journal={BMC Medical Imaging},
  volume={22},
  number={1},
  pages={101},
  year={2022},
  publisher={Springer}
}

@inproceedings{hammernik2017influence,
  title={On the influence of sampling pattern design on deep learning-based MRI reconstruction},
  author={Hammernik, Kerstin and Knoll, Florian and Sodickson, Daniel K and Pock, Thomas},
  booktitle={25th Scientific Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine: ISMRM 2017},
  pages={0644},
  year={2017}
}

@misc{zhang2020extending,
      title={Extending LOUPE for K-space Under-sampling Pattern Optimization in Multi-coil MRI}, 
      author={Jinwei Zhang and Hang Zhang and Alan Wang and Qihao Zhang and Mert Sabuncu and Pascal Spincemaille and Thanh D. Nguyen and Yi Wang},
      year={2020},
      eprint={2007.14450},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

@inproceedings{yin2021end,
  title={End-to-end sequential sampling and reconstruction for mr imaging},
  author={Yin, Tianwei and Wu, Zihui and Sun, He and Dalca, Adrian V and Yue, Yisong and Bouman, Katherine L},
  booktitle={Proceedings of the Machine Learning for Health Conference},
  year={2021}
}

@Article{bioengineering10020158,
AUTHOR = {Radhakrishna, Chaithya Giliyar and Ciuciu, Philippe},
TITLE = {Jointly Learning Non-Cartesian k-Space Trajectories and Reconstruction Networks for 2D and 3D MR Imaging through Projection},
JOURNAL = {Bioengineering},
VOLUME = {10},
YEAR = {2023},
NUMBER = {2},
ARTICLE-NUMBER = {158},
PubMedID = {36829652},
ISSN = {2306-5354},
DOI = {10.3390/bioengineering10020158}
}


@article{Liang2020,
  title = {Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks},
  volume = {37},
  ISSN = {1558-0792},
  DOI = {10.1109/msp.2019.2950557},
  number = {1},
  journal = {IEEE Signal Processing Magazine},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
  author = {Liang,  Dong and Cheng,  Jing and Ke,  Ziwen and Ying,  Leslie},
  year = {2020},
  month = jan,
  pages = {141--151}
}

@article{10.1093/biomet/34.1-2.28,
    author = {WELCH, B. L.},
    title = "{THE GENERALIZATION OF 'STUDENT'S' PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED}",
    journal = {Biometrika},
    volume = {34},
    number = {1-2},
    pages = {28-35},
    year = {1947},
    month = {01},
    issn = {0006-3444},
    doi = {10.1093/biomet/34.1-2.28},
}

@article{YIASEMIS202433,
title = {On retrospective k-space subsampling schemes for deep MRI reconstruction},
journal = {Magnetic Resonance Imaging},
volume = {107},
pages = {33-46},
year = {2024},
issn = {0730-725X},
doi = {https://doi.org/10.1016/j.mri.2023.12.012},
author = {George Yiasemis and Clara I. S\'anchez and Jan-Jakob Sonke and Jonas Teuwen},
keywords = {Deep MRI reconstruction, Retrospective -space subsampling, Non-rectilinear subsampling, Non-Cartesian subsampling, Recurrent Variational network},
}

@misc{paszke2019pytorch,
      title={PyTorch: An Imperative Style, High-Performance Deep Learning Library}, 
      author={Adam Paszke and Sam Gross and Francisco Massa and Adam Lerer and James Bradbury and Gregory Chanan and Trevor Killeen and Zeming Lin and Natalia Gimelshein and Luca Antiga and Alban Desmaison and Andreas K{\"o}pf and Edward Yang and Zach DeVito and Martin Raison and Alykhan Tejani and Sasank Chilamkurthy and Benoit Steiner and Lu Fang and Junjie Bai and Soumith Chintala},
      year={2019},
      eprint={1912.01703},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@InProceedings{10.1007/978-3-319-24574-4_28,
author="Ronneberger, Olaf
and Fischer, Philipp
and Brox, Thomas",
editor="Navab, Nassir
and Hornegger, Joachim
and Wells, William M.
and Frangi, Alejandro F.",
title="U-Net: Convolutional Networks for Biomedical Image Segmentation",
booktitle="Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015",
year="2015",
publisher="Springer International Publishing",
address="Cham",
pages="234--241",
isbn="978-3-319-24574-4"
}

@article{ulmer2022deep,
  title={deep-significance-Easy and Meaningful Statistical Significance Testing in the Age of Neural Networks},
  author={Ulmer, Dennis and Hardmeier, Christian and Frellsen, Jes},
  journal={arXiv preprint arXiv:2204.06815},
  year={2022}
}


@incollection{del2018optimal,
  title={An optimal transportation approach for assessing almost stochastic order},
  author={Del Barrio, Eustasio and Cuesta-Albertos, Juan A and Matr{\'a}n, Carlos},
  booktitle={The Mathematics of the Uncertain},
  pages={33--44},
  year={2018},
  publisher={Springer}
}


@article{paszke2017automatic,
  title={Automatic differentiation in pytorch},
  author={Paszke, Adam and Gross, Sam and Chintala, Soumith and Chanan, Gregory and Yang, Edward and DeVito, Zachary and Lin, Zeming and Desmaison, Alban and Antiga, Luca and Lerer, Adam},
  year={2017}
}



@article{tsao2003k,
  title={k-t BLAST and k-t SENSE: dynamic MRI with high frame rate exploiting spatiotemporal correlations},
  author={Tsao, Jeffrey and Boesiger, Peter and Pruessmann, Klaas P},
  journal={Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine},
  volume={50},
  number={5},
  pages={1031--1042},
  year={2003},
  publisher={Wiley Online Library}
}

@misc{yiasemis2024jssljointsupervisedselfsupervised,
      title={JSSL: Joint Supervised and Self-supervised Learning for MRI Reconstruction}, 
      author={George Yiasemis and Nikita Moriakov and Clara I. S\'anchez and Jan-Jakob Sonke and Jonas Teuwen},
      year={2024},
      eprint={2311.15856},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2311.15856}, 
}

@inproceedings{dror2019deep,
  title={Deep dominance-how to properly compare deep neural models},
  author={Dror, Rotem and Shlomov, Segev and Reichart, Roi},
  booktitle={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
  pages={2773--2785},
  year={2019}
}

@article{wang2025towards,
  title={Towards Universal Learning-based Model for Cardiac Image Reconstruction: Summary of the CMRxRecon2024 Challenge},
  author={Wang, Fanwen and Wang, Zi and Li, Yan and Lyu, Jun and Qin, Chen and Wang, Shuo and Guo, Kunyuan and Sun, Mengting and Huang, Mingkai and Zhang, Haoyu and others},
  journal={arXiv preprint arXiv:2503.03971},
  year={2025}
}

@misc{b6xs-gv29-25,
    doi = {10.21227/b6xs-gv29},
    url = {https://dx.doi.org/10.21227/b6xs-gv29},
    author = {Daryl Xu},
    publisher = {IEEE Dataport},
    title = {CMRxRecon2025},
    year = {2025}
}