@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{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},
  doi={10.1109/JRPROC.1949.232969}}


@article{MontaltTordera2021,
  doi = {10.1016/j.ejmp.2021.02.020},
  url = {https://doi.org/10.1016/j.ejmp.2021.02.020},
  year = {2021},
  month = mar,
  publisher = {Elsevier {BV}},
  volume = {83},
  pages = {79--87},
  author = {Javier Montalt-Tordera and Vivek Muthurangu and Andreas Hauptmann and Jennifer Anne Steeden},
  title = {Machine learning in Magnetic Resonance Imaging: Image reconstruction},
  journal = {Physica Medica}
}
@misc{zbontar2019fastmri,
      title={fastMRI: An Open Dataset and Benchmarks for Accelerated MRI}, 
      author={Jure Zbontar and Florian Knoll and Anuroop Sriram and others},
      year={2019},
      eprint={1811.08839},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@article{Griswold2002,
  doi = {10.1002/mrm.10171},
  url = {https://doi.org/10.1002/mrm.10171},
  year = {2002},
  month = jun,
  publisher = {Wiley},
  volume = {47},
  number = {6},
  pages = {1202--1210},
  author = {Mark A. Griswold and Peter M. Jakob and others},
  title = {Generalized autocalibrating partially parallel acquisitions ({GRAPPA})},
  journal = {Magnetic Resonance in Medicine}
}

@article{Pruessmann1999,
  title={SENSE: sensitivity encoding for fast MRI},
  author={Pruessmann, Klaas P and Weiger, Markus and Scheidegger, Markus B and Boesiger, Peter},
  journal={Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine},
  volume={42},
  number={5},
  pages={952--962},
  year={1999},
  publisher={Wiley Online Library}
}

@ARTICLE{1580791,
  author={Candes, E.J. and Romberg, J. and Tao, T.},
  journal={IEEE Transactions on Information Theory}, 
  title={Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information}, 
  year={2006},
  volume={52},
  number={2},
  pages={489-509},
  doi={10.1109/TIT.2005.862083}}

@ARTICLE{4472246,
  author={Lustig, Michael and Donoho, David L. and Santos, Juan M. and Pauly, John M.},
  journal={IEEE Signal Processing Magazine}, 
  title={Compressed Sensing MRI}, 
  year={2008},
  volume={25},
  number={2},
  pages={72-82},
  doi={10.1109/MSP.2007.914728}}

@book{tikhonov1977solutions,
  added-at = {2011-09-07T17:00:35.000+0200},
  address = {Washington, D.C.: John Wiley \& Sons, New York},
  author = {Tikhonov, Andrey N. and Arsenin, Vasiliy Y.},
  biburl = {https://www.bibsonomy.org/bibtex/2940dd9e8193fd295da5911df36c24851/peter.ralph},
  description = {MR: Publications results for "MR Number=(455365)"},
  interhash = {9aacc055724bc6d774982fca78c5d2d9},
  intrahash = {940dd9e8193fd295da5911df36c24851},
  keywords = {Laplace_inversion inverse_problems laplace_transform},
  mrclass = {65J05 (65R05 65NXX)},
  mrnumber = {0455365 (56 \#13604)},
  mrreviewer = {M. Z. Nashed},
  note = {Translated from the Russian, Preface by translation editor Fritz John, Scripta Series in Mathematics},
  pages = {xiii+258},
  publisher = {V. H. Winston \& Sons},
  timestamp = {2012-03-23T20:17:13.000+0100},
  title = {Solutions of ill-posed problems},
  year = 1977
}

@article{yan2023dc,
  title={DC-SiamNet: Deep contrastive Siamese network for self-supervised MRI reconstruction},
  author={Yan, Yanghui and Yang, Tiejun and Zhao, Xiang and Jiao, Chunxia and Yang, Aolin and Miao, Jianyu},
  journal={Computers in Biology and Medicine},
  volume={167},
  pages={107619},
  year={2023},
  publisher={Elsevier}
} 
@article{Yaman2020,
  doi = {10.1002/mrm.28378},
  url = {https://doi.org/10.1002/mrm.28378},
  year = {2020},
  month = jul,
  publisher = {Wiley},
  volume = {84},
  number = {6},
  pages = {3172--3191},
  author = {Burhaneddin Yaman and Seyed Amir Hossein Hosseini and Steen Moeller and Jutta Ellermann and K{\^{a}}mil U{\u{g}}urbil and Mehmet Ak{\c{c}}akaya},
  title = {Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data},
  journal = {Magnetic Resonance in Medicine}
}


@misc{millard2023,
      title={A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noise}, 
      author={Charles Millard and Mark Chiew},
      year={2022},
      eprint={2205.10278},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

@misc{zhou2022dsformer,
      title={DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction}, 
      author={Bo Zhou and Neel Dey and Jo Schlemper and others},
      year={2022},
      eprint={2201.10776},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}


@inproceedings{korkmaz2023self,
  title={Self-supervised MRI reconstruction with unrolled diffusion models},
  author={Korkmaz, Yilmaz and Cukur, Tolga and Patel, Vishal M},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={491--501},
  year={2023},
  organization={Springer}
}


@misc{cui2022selfscore,
      title={Self-Score: Self-Supervised Learning on Score-Based Models for MRI Reconstruction}, 
      author={Zhuo-Xu Cui and Chentao Cao and Shaonan Liu and others},
      year={2022},
      eprint={2209.00835},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

@incollection{Hu2021,
  doi = {10.1007/978-3-030-87231-1\_37},
  url = {https://doi.org/10.1007/978-3-030-87231-1\_37},
  year = {2021},
  publisher = {Springer International Publishing},
  pages = {382--391},
  author = {Chen Hu and Cheng Li and Haifeng Wang and Qiegen Liu and Hairong Zheng and Shanshan Wang},
  title = {Self-supervised Learning for {MRI} Reconstruction with a Parallel Network Training Framework},
  booktitle = {Medical Image Computing and Computer Assisted Intervention {\textendash} {MICCAI} 2021}
}

@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}
}

@misc{tibrewala2023fastmri,
      title={FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging}, 
      author={Radhika Tibrewala and Tarun Dutt and Angela Tong and Luke Ginocchio and Mahesh B Keerthivasan and Steven H Baete and Sumit Chopra and Yvonne W Lui and Daniel K Sodickson and Hersh Chandarana and Patricia M Johnson},
      year={2023},
      eprint={2304.09254},
      archivePrefix={arXiv},
      primaryClass={physics.med-ph}
}


@article{yiasemis2023retrospective,
  title={On retrospective k-space subsampling schemes for deep MRI reconstruction},
  author={Yiasemis, George and S{\'a}nchez, Clara I and Sonke, Jan-Jakob and Teuwen, Jonas},
  journal={Magnetic Resonance Imaging},
  volume={107},
  pages={33--46},
  year={2024},
  publisher={Elsevier}
}

@InProceedings{10.1007/978-3-030-32251-9_78,
author="Duan, Jinming
and Schlemper, Jo
and Qin, Chen
and Ouyang, Cheng
and Bai, Wenjia
and Biffi, Carlo
and Bello, Ghalib
and Statton, Ben
and O'Regan, Declan P.
and Rueckert, Daniel",
editor="Shen, Dinggang
and Liu, Tianming
and Peters, Terry M.
and Staib, Lawrence H.
and Essert, Caroline
and Zhou, Sean
and Yap, Pew-Thian
and Khan, Ali",
title="VS-Net: Variable Splitting Network for Accelerated Parallel MRI Reconstruction",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2019",
year="2019",
publisher="Springer International Publishing",
pages="713--722",
isbn="978-3-030-32251-9"
}



@article{https://doi.org/10.1002/mrm.10087,
  title={Self-calibrating parallel imaging with automatic coil sensitivity extraction},
  author={McKenzie, Charles A and Yeh, Ernest N and Ohliger, Michael A and Price, Mark D and Sodickson, Daniel K},
  journal={Magnetic Resonance in Medicine},
  volume={47},
  number={3},
  pages={529--538},
  year={2002},
  publisher={Wiley Online Library}
}

@InProceedings{yiasemis2023deep,
  title={Deep cardiac MRI reconstruction with ADMM},
  author={Yiasemis, George and Moriakov, Nikita and Sonke, Jan-Jakob and Teuwen, Jonas},
  booktitle={International Workshop on Statistical Atlases and Computational Models of the Heart},
  pages={479--490},
  year={2023},
  organization={Springer}
}

@misc{hu2024spicerselfsupervisedlearningmri,
      title={SPICER: Self-Supervised Learning for MRI with Automatic Coil Sensitivity Estimation and Reconstruction}, 
      author={Yuyang Hu and Weijie Gan and Chunwei Ying and others},
      year={2024},
      eprint={2210.02584},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2210.02584}, 
}

@article{Uecker2013,
  url = {https://doi.org/10.1002/mrm.24751},
  year = {2013},
  month = may,
  publisher = {Wiley},
  volume = {71},
  number = {3},
  pages = {990--1001},
  author = {Martin Uecker and Peng Lai and Mark J. Murphy and others},
  title = {{ESPIRiT}-an eigenvalue approach to autocalibrating parallel {MRI}: Where {SENSE} meets {GRAPPA}},
  journal = {Magnetic Resonance in Medicine}
}

@article{yiasemis2023vsharp,
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{Pal2022-ce,
  title    = "A review and experimental evaluation of deep learning methods for
              {MRI} reconstruction",
  author   = "Pal, Arghya and Rathi, Yogesh",
  abstract = "Following the success of deep learning in a wide range of
              applications, neural network-based machine-learning techniques
              have received significant interest for accelerating magnetic
              resonance imaging (MRI) acquisition and reconstruction
              strategies. A number of ideas inspired by deep learning
              techniques for computer vision and image processing have been
              successfully applied to nonlinear image reconstruction in the
              spirit of compressed sensing for accelerated MRI. Given the
              rapidly growing nature of the field, it is imperative to
              consolidate and summarize the large number of deep learning
              methods that have been reported in the literature, to obtain a
              better understanding of the field in general. This article
              provides an overview of the recent developments in neural-network
              based approaches that have been proposed specifically for
              improving parallel imaging. A general background and introduction
              to parallel MRI is also given from a classical view of k-space
              based reconstruction methods. Image domain based techniques that
              introduce improved regularizers are covered along with k-space
              based methods which focus on better interpolation strategies
              using neural networks. While the field is rapidly evolving with
              plenty of papers published each year, in this review, we attempt
              to cover broad categories of methods that have shown good
              performance on publicly available data sets. Limitations and open
              problems are also discussed and recent efforts for producing open
              data sets and benchmarks for the community are examined.",
  journal  = "J. Mach. Learn. Biomed. Imaging",
  volume   =  1,
  month    =  mar,
  year     =  2022,
  keywords = "Deep Leaning; MRI Reconstruction; k-space reconstruction; machine
              learning; parallel MRI",
  language = "en"
}


@article{Liu2017,
  doi = {10.3390/s17030509},
  url = {https://doi.org/10.3390/s17030509},
  year = {2017},
  month = mar,
  publisher = {{MDPI} {AG}},
  volume = {17},
  number = {3},
  pages = {509},
  author = {Ryan Liu and Lin Shi and Simon Yu and Naixue Xiong and Defeng Wang},
  title = {Reconstruction of Undersampled Big Dynamic {MRI} Data Using Non-Convex Low-Rank and Sparsity Constraints},
  journal = {Sensors}
}

@article{Uecker2010,
  doi = {10.1002/nbm.1585},
  url = {https://doi.org/10.1002/nbm.1585},
  year = {2010},
  month = aug,
  publisher = {Wiley},
  volume = {23},
  number = {8},
  pages = {986--994},
  author = {Martin Uecker and Shuo Zhang and Dirk Voit and Alexander Karaus and Klaus-Dietmar Merboldt and Jens Frahm},
  title = {Real-time {MRI} at a resolution of 20 ms},
  journal = {{NMR} in Biomedicine}
}

@ARTICLE{Sarma2014,
  title     = "Accelerating dynamic magnetic resonance imaging ({MRI}) for lung
               tumor tracking based on low-rank decomposition in the
               spatial-temporal domain: a feasibility study based on simulation
               and preliminary prospective undersampled {MRI}",
  author    = "Sarma, Manoj and Hu, Peng and Rapacchi, Stanislas and others",

  journal   = "Int. J. Radiat. Oncol. Biol. Phys.",
  publisher = "Elsevier BV",
  volume    =  88,
  number    =  3,
  pages     = "723--731",
  month     =  mar,
  year      =  2014,
  language  = "en"
}



@article{Kim2021,
  doi = {10.1016/j.ejrad.2021.110012},
  url = {https://doi.org/10.1016/j.ejrad.2021.110012},
  year = {2021},
  month = dec,
  publisher = {Elsevier {BV}},
  volume = {145},
  pages = {110012},
  author = {Eu Hyun Kim and Moon Hyung Choi and Young Joon Lee and Dongyeob Han and Mahmoud Mostapha and Dominik Nickel},
  title = {Deep learning-accelerated T2-weighted imaging of the prostate: Impact of further acceleration with lower spatial resolution on image quality},
  journal = {European Journal of Radiology}
}

@misc{fessler2019optimization,
      title={Optimization methods for MR image reconstruction}, 
      author={Jeffrey A Fessler},
      year={2019},
      eprint={1903.03510},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

@inproceedings{Korkmaz2023,
  title={Self-supervised MRI reconstruction with unrolled diffusion models},
  author={Korkmaz, Yilmaz and Cukur, Tolga and Patel, Vishal M},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={491--501},
  year={2023},
  organization={Springer}
}

@article{Hammernik2017,
  doi = {10.1002/mrm.26977},
  url = {https://doi.org/10.1002/mrm.26977},
  year = {2017},
  month = nov,
  publisher = {Wiley},
  volume = {79},
  number = {6},
  pages = {3055--3071},
  author = {Kerstin Hammernik and Teresa Klatzer and Erich Kobler and Michael P. Recht and Daniel K. Sodickson and Thomas Pock and Florian Knoll},
  title = {Learning a variational network for reconstruction of accelerated {MRI} data},
  journal = {Magnetic Resonance in Medicine}
}


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

@Article{bioengineering10091012,
AUTHOR = {Singh, Dilbag and Monga, Anmol and de Moura, Hector L. and Zhang, Xiaoxia and Zibetti, Marcelo and Regatte, Ravinder },
TITLE = {Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review},
JOURNAL = {Bioengineering},
VOLUME = {10},
YEAR = {2023},
NUMBER = {9},
ARTICLE-NUMBER = {1012},
URL = {https://www.mdpi.com/2306-5354/10/9/1012},
PubMedID = {37760114},
ISSN = {2306-5354},
ABSTRACT = {Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides excellent soft-tissue contrast and high-resolution images of the human body, allowing us to understand detailed information on morphology, structural integrity, and physiologic processes. However, MRI exams usually require lengthy acquisition times. Methods such as parallel MRI and Compressive Sensing (CS) have significantly reduced the MRI acquisition time by acquiring less data through undersampling k-space. The state-of-the-art of fast MRI has recently been redefined by integrating Deep Learning (DL) models with these undersampled approaches. This Systematic Literature Review (SLR) comprehensively analyzes deep MRI reconstruction models, emphasizing the key elements of recently proposed methods and highlighting their strengths and weaknesses. This SLR involves searching and selecting relevant studies from various databases, including Web of Science and Scopus, followed by a rigorous screening and data extraction process using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. It focuses on various techniques, such as residual learning, image representation using encoders and decoders, data-consistency layers, unrolled networks, learned activations, attention modules, plug-and-play priors, diffusion models, and Bayesian methods. This SLR also discusses the use of loss functions and training with adversarial networks to enhance deep MRI reconstruction methods. Moreover, we explore various MRI reconstruction applications, including non-Cartesian reconstruction, super-resolution, dynamic MRI, joint learning of reconstruction with coil sensitivity and sampling, quantitative mapping, and MR fingerprinting. This paper also addresses research questions, provides insights for future directions, and emphasizes robust generalization and artifact handling. Therefore, this SLR serves as a valuable resource for advancing fast MRI, guiding research and development efforts of MRI reconstruction for better image quality and faster data acquisition.},
DOI = {10.3390/bioengineering10091012}
}

@article{DIRECTTOOLKIT,
    doi = {10.21105/joss.04278},
    url = {https://doi.org/10.21105/joss.04278},
    year = {2022},
    publisher = {The Open Journal},
    volume = {7},
    number = {73},
    pages = {4278},
    author = {George Yiasemis and Nikita Moriakov and Dimitrios Karkalousos and Matthan Caan and Jonas Teuwen},
    title = {DIRECT: Deep Image REConstruction Toolkit},
    journal = {Journal of Open Source Software}
}

@misc{kingma2017adam,
      title={Adam: A Method for Stochastic Optimization}, 
      author={Diederik P. Kingma and Jimmy Ba},
      year={2017},
      eprint={1412.6980},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@incollection{Ronneberger2015,
  doi = {10.1007/978-3-319-24574-4\_28},
  url = {https://doi.org/10.1007/978-3-319-24574-4\_28},
  year = {2015},
  publisher = {Springer International Publishing},
  pages = {234--241},
  author = {Olaf Ronneberger and Philipp Fischer and Thomas Brox},
  title = {U-Net: Convolutional Networks for Biomedical Image Segmentation},
  booktitle = {Lecture Notes in Computer Science}
}

@inbook{Sriram2020,
  title = {End-to-End Variational Networks for Accelerated MRI Reconstruction},
  ISBN = {9783030597139},
  ISSN = {1611-3349},
  url = {http://dx.doi.org/10.1007/978-3-030-59713-9\_7},
  DOI = {10.1007/978-3-030-59713-9\_7},
  booktitle = {Lecture Notes in Computer Science},
  publisher = {Springer International Publishing},
  author = {Sriram,  Anuroop and Zbontar,  Jure and Murrell,  Tullie and others},
  year = {2020},
  pages = {64–73}
}
@ARTICLE{5617283,
  author={Ravishankar, Saiprasad and Bresler, Yoram},
  journal={IEEE Transactions on Medical Imaging}, 
  title={MR Image Reconstruction From Highly Undersampled k-Space Data by Dictionary Learning}, 
  year={2011},
  volume={30},
  number={5},
  pages={1028-1041},
  doi={10.1109/TMI.2010.2090538}}

@article{desai2021noise2recon,
author = {Desai, Arjun D. and Ozturkler, Batu M. and Sandino, Christopher M. and others},
title = {Noise2Recon: Enabling SNR-robust MRI reconstruction with semi-supervised and self-supervised learning},
journal = {Magnetic Resonance in Medicine},
volume = {90},
number = {5},
pages = {2052-2070},
keywords = {data efficiency, deep learning, denoising, distribution shift, image reconstruction, self-supervised learning, semi-supervised learning},
doi = {https://doi.org/10.1002/mrm.29759},

year = {2023}
}


@InProceedings{pmlr-v227-zhang24a,
  title = 	 {Zero-Shot Self-Supervised Joint Temporal Image and Sensitivity Map Reconstruction via Linear Latent Space},
  author =       {Zhang, Molin and Xu, Junshen and Arefeen, Yamin and Adalsteinsson, Elfar},
  booktitle = 	 {Medical Imaging with Deep Learning},
  pages = 	 {1713--1725},
  year = 	 {2024},
  editor = 	 {Oguz, Ipek and Noble, Jack and Li, Xiaoxiao and Styner, Martin and Baumgartner, Christian and Rusu, Mirabela and Heinmann, Tobias and Kontos, Despina and Landman, Bennett and Dawant, Benoit},
  volume = 	 {227},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {10--12 Jul},
  publisher =    {PMLR},
}

@misc{darestani2022testtimetrainingclosenatural,
      title={Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing}, 
      author={Mohammad Zalbagi Darestani and Jiayu Liu and Reinhard Heckel},
      year={2022},
      eprint={2204.07204},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2204.07204}, 
}

@article{ZHOU2022102538,
  title={Dual-domain self-supervised learning for accelerated non-Cartesian MRI reconstruction},
  author={Zhou, Bo and Schlemper, Jo and Dey, Neel and others},
  journal={Medical Image Analysis},
  volume={81},
  pages={102538},
  year={2022},
  publisher={Elsevier}
}

@article{SHAPIRO1965,
  title = {An analysis of variance test for normality (complete samples)},
  volume = {52},
  ISSN = {1464-3510},
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@inproceedings{batson2019noise2self,
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@misc{cmrxrecon,
  doi = {10.48550/ARXIV.2404.01082},
  url = {https://arxiv.org/abs/2404.01082},
  author = {Lyu,  Jun and Qin,  Chen and Wang,  Shuo and others},
  keywords = {Image and Video Processing (eess.IV),  FOS: Electrical engineering,  electronic engineering,  information engineering,  FOS: Electrical engineering,  electronic engineering,  information engineering},
  title = {The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023},
  publisher = {arXiv},
  year = {2024},
  copyright = {arXiv.org perpetual,  non-exclusive license}
}


@article{cmrxrecondataset,
  title={CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI},
  author = {Wang,  Chengyan and Lyu,  Jun and Wang,  Shuo and others},
  journal={Scientific Data},
  volume={11},
  number={1},
  pages={687},
  year={2024},
  publisher={Nature Publishing Group UK London}
}