@article{zufiria2019feature,
  title={A feature-based convolutional neural network for reconstruction of interventional \uppercase{MRI}},
  author={{B. Zufiria}\textsuperscript{*} and {S. Qiu}\textsuperscript{*} and \textbf{K. Yan} and Zhao, Ruiyang and Wang, Runke and She, Huajun and Zhang, Chengcheng and Sun, Bomin and Herman, Pawel and Du, Yiping and others},
  journal={NMR in Biomedicine},
  pages={e4231},
  year={2019}
}

@article{LYU2025103485,
title = {The state-of-the-art in cardiac MRI reconstruction: Results of the CMRxRecon challenge in MICCAI 2023},
journal = {Medical Image Analysis},
volume = {101},
pages = {103485},
year = {2025},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2025.103485},
url = {https://www.sciencedirect.com/science/article/pii/S1361841525000337},
author = {Jun Lyu and Chen Qin and Shuo Wang and Fanwen Wang and Yan Li and Zi Wang and Kunyuan Guo and Cheng Ouyang and Michael Tänzer and Meng Liu and Longyu Sun and Mengting Sun and Qing Li and Zhang Shi and Sha Hua and Hao Li and Zhensen Chen and Zhenlin Zhang and Bingyu Xin and Dimitris N. Metaxas and George Yiasemis and Jonas Teuwen and Liping Zhang and Weitian Chen and Yidong Zhao and Qian Tao and Yanwei Pang and Xiaohan Liu and Artem Razumov and Dmitry V. Dylov and Quan Dou and Kang Yan and Yuyang Xue and Yuning Du and Julia Dietlmeier and Carles Garcia-Cabrera and Ziad {Al-Haj Hemidi} and Nora Vogt and Ziqiang Xu and Yajing Zhang and Ying-Hua Chu and Weibo Chen and Wenjia Bai and Xiahai Zhuang and Jing Qin and Lianming Wu and Guang Yang and Xiaobo Qu and He Wang and Chengyan Wang},
keywords = {Reconstruction, Cardiac imaging, Fast imaging, Under-sampling, K-space},
abstract = {Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial–temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techniques, physical modeling, and model complexity, thereby providing valuable insights for further developments in this field.}
}

@inproceedings{10.1007/978-3-031-52448-6_37,
author="{Q. Dou}\textsuperscript{*}
and \textbf{K. Yan}\textsuperscript{*}
and {S. Chen}\textsuperscript{*}
and {Z. Wang}\textsuperscript{*}
and Feng, Xue
and Meyer, Craig H.",
title="$C^3$-Net: Complex-Valued Cascading Cross-Domain Convolutional Neural Network for Reconstructing Undersampled \uppercase{CMR} Images",
booktitle="Statistical Atlases and Computational Models of the Heart. Regular and CMR$\times$Recon Challenge Papers",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="390--399",
isbn="978-3-031-52448-6"
}
@inproceedings{kang2025ISMRM,
    author = "\textbf{K. Yan} and Dou, Quan and Wang, Zhixing and Xue, Feng and Meyer, Craig H.",
    title = "Optimization of free-breathing spiral cardiac cine imaging at 3\uppercase{T} with variable flip angle scheme and region-optimized virtual coils (\uppercase{ROV}ir)",
    booktitle = "ISMRM",
    year ="2025",
    addendum = {[Oral presentation]}
}
@inproceedings{kang2024ISMRM,
    author = "\textbf{K. Yan} and Dou, Quan and Meyer, Craig H.",
    title = "Multi-dimensional denoising of diffusion \uppercase{MRI} using low rank dictionary learning",
    booktitle = "ISMRM",
    year ="2024",
}
@inproceedings{kang2023ISMRM,
    author = "\textbf{K. Yan} and Meyer, Craig H.",
    title = "Accelerated parameter mapping in the k-p domain via nonconvex low rank constraint",
    booktitle = "ISMRM",
    year = "2023"
}
@inproceedings{kang2022ISMRM1,
    author = "\textbf{K. Yan} and Wang, Zhixing and Dou, Quan and Chen, Sheng and Meyer, Craig H.",
    title = "Applying advanced denoisers to enhance highly undersampled MRI reconstruction under plug-and-play \uppercase{ADMM} framework",
    booktitle = "ISMRM",
    year = "2022"
}
@inproceedings{kang2022ISMRM2,
    author = "\textbf{K. Yan} and She, Huajun and Du, Yiping P.",
    title = "Simultaneous \uppercase{ADC} mapping and water-fat separation with $\uppercase{B}_{0}$ correction using a rosette acquisition",
    booktitle = "ISMRM",
    year = "2022"
}
@inproceedings{kang2020ISMRM1,
    author = "Zhang, Yufei and Wang, Zhijun and Chen, Quan and Li, Shou and Ding, ZeKang and Shen, Chenfei and Chen, Xudong and \textbf{K. Yan} and Zhang, Chong and Zhou, Xiaodong and Du, Yiping P. and She, Huajun",
    title = "Dynamic Real-time \uppercase{MRI} with Deep Convolutional Recurrent Neural Networks and Non-Cartesian Fidelity",
    booktitle = "ISMRM",
    year = "2020",
    addendum = {[Oral presentation]}
}
@inproceedings{kang2020ISMRM2,
    author = "Zhao, Ruiyang and Wang, Tao, and \textbf{K. Yan} and Zhang, Chengcheng and Liang, Zhipei and Du, Yiping P. and Li, Dianyou and Sun, Bomin and Feng, Yuan",
    title = "A Recurrent Neural Network (\uppercase{RNN}) based reconstruction of extremely undersampled neuro-interventional \uppercase{MRI}",
    booktitle = "ISMRM",
    year = "2020"
}
@inproceedings{kang2019ISMRM1,
    author = "Li, Shuo and Chen, Xi, and \textbf{K. Yan} and Chen, Xudong and Gong, Xiaomao and Xu, Jian and Liu, Qi and Chen, Quan and She, Huajun and Du, Yiping P.",
    title = "Dynamic 3\uppercase{D} lung \suppercase{MRI} using the stack-of-stars sequence with \uppercase{SI} navigation",
    booktitle = "ISMRM",
    year = "2019"
}
@inproceedings{kang2019ISMRM2,
    author = "She, Huajun and Chen, Quan, and Li, Shuo and \textbf{K. Yan} and Chen, Xudong and Chen, Xi and Feng, Yuan and Keupp, Jochen and Lenkinski, Robert and Vinogradov, Elena and Du, Yiping P.",
    title = "Accelerate Parallel \uppercase{CEST} Imaging with Dynamic Convolutional Recurrent Neural Network",
    booktitle = "ISMRM",
    year = "2019",
    addendum = {[Oral presentation]}
}
@inproceedings{kang2019ISMRM3,
    author = "\textbf{K. Yan} and Zufiria, Blanca and Singer, Alexa and Chen, Xudong and Yang, Zhiyu and Li, Shuo and Qiu, Suhao and She, Huajun and Sun, Bomin and Du, Yiping P. and Liang, Zhipei and Feng, Yuan",
    title = "A novel feature-based image reconstruction for neuro-interventional \uppercase{MRI}",
    booktitle = "ISMRM",
    year = "2019"
}


@misc{allen2023arxiv,
  title={Long Spiral \uppercase{MRI} Thermometry: A Report},
  author={Allen, Steven P and Chen, Sheng and \textbf{K. Yan} and Meyer, Craig H},
  year={2023},
  publisher={FocUS Archive},
}
@misc{feng2019brain,
  title={brain tissue rapid imaging and image reconstruction method for magnetic resonance navigation},
  author={Feng, Yuan and Zufiria, Blanca and Qiu, Suhao and \textbf{K. Yan} and Zhao, Ruiyang and Zhou, Xiaodong and Zhang, Chong and Du, Yiping P. and Liang, Zhipei},
  year={2019},
  note={CN109872377A},
  keywords={patent}
}