{"type":"ScholarlyArticle","title":"Deep Unfolding Convolutional Dictionary Model for Multi-Contrast MRI Super-resolution and Reconstruction","authors":[{"name":"Pengcheng Lei","slug":"pengcheng-lei","given":"Pengcheng","family":"Lei","affiliation":"School of Computer Science and Technology , East China Normal University"},{"name":"Faming Fang","slug":"faming-fang","given":"Faming","family":"Fang","affiliation":"School of Computer Science and Technology , East China Normal University"},{"name":"Guixu Zhang","slug":"guixu-zhang","given":"Guixu","family":"Zhang","affiliation":"School of Computer Science and Technology , East China Normal University"},{"name":"Ming Xu","slug":"ming-xu","given":"Ming","family":"Xu","affiliation":"Software Engineering Institute , East China Normal University"}],"abstract":"Magnetic resonance imaging (MRI) tasks often involve multiple contrasts. Recently, numerous deep learning-based multi-contrast MRI super-resolution (SR) and reconstruction methods have been proposed to explore the complementary information from the multi-contrast images. However, these methods either construct parameter-sharing networks or manually design fusion rules, failing to accurately model the correlations between multi-contrast images and lacking certain interpretations. In this paper, we propose a multi-contrast convolutional dictionary (MC-CDic) model under the guidance of the optimization algorithm with a well-designed data fidelity term. Specifically, we bulid an observation model for the multi-contrast MR images to explicitly model the multi-contrast images as common features and unique features. In this way, only the useful information in the reference image can be transferred to the target image, while the inconsistent information will be ignored. We employ the proximal gradient algorithm to optimize the model and unroll the iterative steps into a deep CDic model. Especially, the proximal operators are replaced by learnable ResNet. In addition, multi-scale dictionaries are introduced to further improve the model performance. We test our MC-CDic model on multi-contrast MRI SR and reconstruction tasks. Experimental results demonstrate the superior performance of the proposed MC-CDic model against existing SOTA methods. Code is available at https://github.com/lpcccc-cv/MC-CDic.","doi":"10.24963/ijcai.2023/112","url":"https://ijcai.org/proceedings/2023/112","pdf":"https://ijcai.org/proceedings/2023/0112.pdf","markdown":"https://ijcai.org/proceedings/2023/112.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/112","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":112,"pages":{"first":1008,"last":1016},"keywords":["Computer Vision: CV: Biomedical image analysis"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal et al., 2018] Hemant K Aggarwal, Merry P Mani, and Mathews Jacob. MoDL: Model-based deep learning architecture for inverse problems. IEEE Transactions on Medical Imaging, 38(2):394-405, 2018.","title":"MoDL: Model-based deep learning architecture for inverse problems","authors":"Merry P Hemant K Aggarwal, Mathews Mani, Jacob","year":"2018","venue":"IEEE Transactions on Medical Imaging","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Beck and Teboulle, 2009] Amir Beck and Marc Teboulle. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences, 2(1):183-202, 2009.","title":"A fast iterative shrinkage-thresholding algorithm for linear inverse problems","authors":"Amir Beck, Marc Teboulle","year":"2009","venue":"SIAM Journal on Imaging Sciences","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Dar et al., 2020] Salman UH Dar, Mahmut Yurt, Moham- mad Shahdloo, Muhammed Emrullah Ildız, Berk Tınaz, and Tolga C ¸ukur. Prior-guided image reconstruction for accelerated multi-contrast MRI via generative adversarial networks. IEEE Journal of Selected Topics in Signal Pro- cessing, 14(6):1072-1087, 2020.","title":"Prior-guided image reconstruction for accelerated multi-contrast MRI via generative adversarial networks","authors":"Mahmut Salman Uh Dar, Mohammad Yurt, Shahdloo, Emrullah Muhammed, Berk Ildız, Tolga C Tınaz, ¸ukur","year":"2020","venue":"IEEE Journal of Selected Topics in Signal Processing","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Deng and Dragotti, 2020] Xin Deng and Pier Luigi Dragotti. Deep convolutional neural network for multi-modal image restoration and fusion. IEEE Trans- actions on Pattern Analysis and Machine Intelligence, 43(10):3333-3348, 2020.","title":"Deep convolutional neural network for multi-modal image restoration and fusion","authors":"Xin Deng, Pier Luigi, Dragotti","year":"2020","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Donoho, 1995] David L Donoho. De-noising by soft- thresholding. IEEE Transactions on Information Theory, 41(3):613-627, 1995.","title":"De-noising by softthresholding","authors":"L David, Donoho","year":"1995","venue":"IEEE Transactions on Information Theory","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Fang et al., 2022] Chaowei Fang, Dingwen Zhang, Liang Wang, Yulun Zhang, Lechao Cheng, and Junwei Han. Cross-modality high-frequency transformer for MR image super-resolution. In Proceedings of the 30th ACM Interna- tional Conference on Multimedia, pages 1584-1592, 2022.","title":"Cross-modality high-frequency transformer for MR image super-resolution","authors":"Fang","year":"2022","venue":"Proceedings of the 30th ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Feng et al., 2021] Chun-Mei Feng, Huazhu Fu, Shuhao Yuan, and Yong Xu. Multi-contrast MRI super-resolution via a multi-stage integration network. In International Conference on Medical Image Computing and Computer- Assisted Intervention, pages 140-149. Springer, 2021.","title":"Multi-contrast MRI super-resolution via a multi-stage integration network","authors":"Chun-Mei Feng, Huazhu Fu, Shuhao Yuan, Yong Xu","year":"2021","venue":"International Conference on Medical Image Computing and Computer-Assisted Intervention","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Feng et al., 2022] Chun-Mei Feng, Yunlu Yan, Geng Chen, Yong Xu, Ying Hu, Ling Shao, and Huazhu Fu. Multi- modal transformer for accelerated MR imaging. IEEE Transactions on Medical Imaging, pages 1-1, 2022.","title":"Multimodal transformer for accelerated MR imaging","authors":"Chun-Mei Feng, Yunlu Yan, Geng Chen, Yong Xu, Ying Hu, Ling Shao, Huazhu Fu","year":"2022","venue":"IEEE Transactions on Medical Imaging","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Kim et al., 2016] Jiwon Kim, Jung Kwon Lee, and Ky- oung Mu Lee. Accurate image super-resolution using very deep convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1646-1654, 2016.","title":"Accurate image super-resolution using very deep convolutional networks","authors":"Jiwon Kim, Jung Kwon Lee, Kyoung Mu, Lee","year":"2016","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Lecouat et al., 2020a] Bruno Lecouat, Jean Ponce, and Julien Mairal. A flexible framework for designing train- able priors with adaptive smoothing and game encod- ing. Advances in Neural Information Processing Systems, 33:15664-15675, 2020.","title":"A flexible framework for designing trainable priors with adaptive smoothing and game encoding","authors":"Lecouat","year":"2020","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Lecouat et al., 2020b] Bruno Lecouat, Jean Ponce, and Julien Mairal. Fully trainable and interpretable non-local sparse models for image restoration. In European Confer- ence on Computer Vision, pages 238-254. Springer, 2020.","title":"Fully trainable and interpretable non-local sparse models for image restoration","authors":"Lecouat","year":"2020","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Li et al., 2022] Guangyuan Li, Jun Lv, Yapeng Tian, Qi Dou, Chengyan Wang, Chenliang Xu, and Jing Qin. Transformer-empowered multi-scale contextual matching and aggregation for multi-contrast MRI super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20636-20645, 2022.","title":"Transformer-empowered multi-scale contextual matching and aggregation for multi-contrast MRI super-resolution","authors":"Guangyuan Li, Jun Lv, Yapeng Tian, Qi Dou, Chengyan Wang, Chenliang Xu, Jing Qin","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Liang et al., 2021] Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. Swinir: Image restoration using swin transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, pages 1833-1844, 2021.","title":"Swinir: Image restoration using swin transformer","authors":"Liang","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Liu and Lei, 2021] Cong Liu and Pengcheng Lei. An ef- ficient group skip-connecting network for image super- resolution. Knowledge-Based Systems, 222:107017, 2021.","title":"An efficient group skip-connecting network for image superresolution","authors":"Lei Liu, Cong Liu, Pengcheng Lei","year":"2021","venue":"Knowledge-Based Systems","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Liu et al., 2019] Risheng Liu, Shichao Cheng, Long Ma, Xin Fan, and Zhongxuan Luo. Deep proximal un- rolling: Algorithmic framework, convergence analysis and applications. IEEE Transactions on Image Processing, 28(10):5013-5026, 2019.","title":"Deep proximal unrolling: Algorithmic framework, convergence analysis and applications","authors":"Risheng Liu, Shichao Cheng, Long Ma, Xin Fan, Zhongxuan Luo","year":"2019","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Liu et al., 2021] Risheng Liu, Jinyuan Liu, Zhiying Jiang, Xin Fan, and Zhongxuan Luo. A bilevel integrated model with data-driven layer ensemble for multi-modality image fusion. IEEE Transactions on Image Processing, 30:1261- 1274, 2021.","title":"A bilevel integrated model with data-driven layer ensemble for multi-modality image fusion","authors":"Risheng Liu, Jinyuan Liu, Zhiying Jiang, Xin Fan, Zhongxuan Luo","year":"2021","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Liu et al., 2022] Tianlin Liu, Anadi Chaman, David Belius, and Ivan Dokmanić. Learning multiscale convolutional dictionaries for image reconstruction. IEEE Transactions on Computational Imaging, 8:425-437, 2022.","title":"Learning multiscale convolutional dictionaries for image reconstruction","authors":"Liu","year":"2022","venue":"IEEE Transactions on Computational Imaging","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Lu et al., 2021] Liying Lu, Wenbo Li, Xin Tao, Jiangbo Lu, and Jiaya Jia. MASA-SR: Matching acceleration and spa- tial adaptation for reference-based image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6368-6377, 2021.","title":"MASA-SR: Matching acceleration and spatial adaptation for reference-based image super-resolution","authors":"Liying Lu, Wenbo Li, Xin Tao, Jiangbo Lu, Jiaya Jia","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Lyu et al., 2020] Qing Lyu, Hongming Shan, Cole Ste- ber, Corbin Helis, Chris Whitlow, Michael Chan, and Ge Wang. Multi-contrast super-resolution MRI through a progressive network. IEEE Transactions on Medical Imaging, 39(9):2738-2749, 2020.","title":"Multi-contrast super-resolution MRI through a progressive network","authors":"Qing Lyu, Hongming Shan, Cole Steber, Corbin Helis, Chris Whitlow, Michael Chan, Ge Wang","year":"2020","venue":"IEEE Transactions on Medical Imaging","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Menze et al., 2015] Bjoern H. Menze, Andras Jakab, Stefan Bauer, and Kalpathy-Cramer. The multimodal brain tu- mor image segmentation benchmark (brats). IEEE Trans- actions on Medical Imaging, 34(10):1993-2024, 2015.","title":"The multimodal brain tumor image segmentation benchmark (brats)","authors":"H Bjoern, Andras Menze, Stefan Jakab, Kalpathy-Cramer Bauer","year":"2015","venue":"IEEE Transactions on Medical Imaging","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Nitski et al., 2020] Osvald Nitski, Sayan Nag, Chris McIn- tosh, and Bo Wang. CDF-Net: Cross-domain fusion net- work for accelerated MRI reconstruction. In International Conference on Medical Image Computing and Computer- Assisted Intervention, pages 421-430. Springer, 2020.","title":"CDF-Net: Cross-domain fusion network for accelerated MRI reconstruction","authors":"Osvald Nitski, Sayan Nag, Chris Mcintosh, Bo Wang","year":"2020","venue":"International Conference on Medical Image Computing and Computer-Assisted Intervention","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Ronneberger et al., 2015] Olaf Ronneberger, Philipp Fis- cher, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer- Assisted Intervention, pages 234-241. Springer, 2015.","title":"Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation","authors":"Ronneberger","year":"2015","venue":"International Conference on Medical Image Computing and Computer-Assisted Intervention","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Simon and Elad, 2019] Dror Simon and Michael Elad. Re- thinking the CSC model for natural images. Advances in Neural Information Processing Systems, 32:2271-2281, 2019.","title":"Rethinking the CSC model for natural images","authors":"Dror Simon, Michael Elad","year":"2019","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Song et al., 2019a] Pingfan Song, Lior Weizman, João FC Mota, Yonina C Eldar, and Miguel RD Rodrigues. Cou- pled dictionary learning for multi-contrast MRI recon- struction. IEEE Transactions on Medical Imaging, 39(3):621-633, 2019.","title":"Coupled dictionary learning for multi-contrast MRI reconstruction","authors":"Song","year":"2019","venue":"IEEE Transactions on Medical Imaging","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Song et al., 2019b] Pingfan Song, Lior Weizman, João FC Mota, Yonina C Eldar, and Miguel RD Rodrigues. Cou- pled dictionary learning for multi-contrast MRI recon- struction. IEEE Transactions on Medical Imaging, 39(3):621-633, 2019.","title":"Coupled dictionary learning for multi-contrast MRI reconstruction","authors":"Song","year":"2019","venue":"IEEE Transactions on Medical Imaging","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Sriram et al., 2020] Anuroop Sriram, Jure Zbontar, Tullie Murrell, Aaron Defazio, C Lawrence Zitnick, Nafissa Yakubova, Florian Knoll, and Patricia Johnson. End-to- end variational networks for accelerated MRI reconstruc- tion. In International Conference on Medical Image Com- puting and Computer-Assisted Intervention, pages 64-73. Springer, 2020.","title":"End-toend variational networks for accelerated MRI reconstruction","authors":"Sriram","year":"2020","venue":"International Conference on Medical Image Computing and Computer-Assisted Intervention","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Sun et al., 2019] Liyan Sun, Zhiwen Fan, Xueyang Fu, Yue Huang, Xinghao Ding, and John Paisley. A deep informa- tion sharing network for multi-contrast compressed sens- ing MRI reconstruction. IEEE Transactions on Image Pro- cessing, 28(12):6141-6153, 2019.","title":"A deep information sharing network for multi-contrast compressed sensing MRI reconstruction","authors":"Liyan Sun, Zhiwen Fan, Xueyang Fu, Yue Huang, Xinghao Ding, John Paisley","year":"2019","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Wang et al., 2016] Shanshan Wang, Zhenghang Su, Leslie Ying, Xi Peng, Shun Zhu, Feng Liang, Dagan Feng, and Dong Liang. Accelerating magnetic resonance imag- ing via deep learning. In IEEE 13th International Sym- posium on Biomedical Imaging (ISBI), pages 514-517. IEEE, 2016.","title":"Accelerating magnetic resonance imaging via deep learning","authors":"Shanshan Wang, Zhenghang Su, Leslie Ying, Xi Peng, Shun Zhu, Feng Liang, Dagan Feng, Dong Liang","year":"2016","venue":"IEEE 13th International Symposium on Biomedical Imaging (ISBI)","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Wang et al., 2020] Hong Wang, Qi Xie, Qian Zhao, and Deyu Meng. A model-driven deep neural network for sin- gle image rain removal. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3103-3112, June 2020.","title":"A model-driven deep neural network for single image rain removal","authors":"Wang","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Wang et al., 2021] Haoqian Wang, Xiaowan Hu, Xiaole Zhao, and Yulun Zhang. Wide weighted attention multi- scale network for accurate MR image super-resolution. IEEE Transactions on Circuits and Systems for Video Technology, 32(3):962-975, 2021.","title":"Wide weighted attention multiscale network for accurate MR image super-resolution","authors":"Wang","year":"2021","venue":"IEEE Transactions on Circuits and Systems for Video Technology","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Wang et al., 2022] Hong Wang, Yuexiang Li, Deyu Meng, and Yefeng Zheng. Adaptive convolutional dictionary net- work for CT metal artifact reduction. In The 31st Inter- national Joint Conference on Artificial Intelligence, pages 1401-1407, 2022.","title":"Adaptive convolutional dictionary network for CT metal artifact reduction","authors":"Wang","year":"2022","venue":"The 31st International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2022/195"},{"position":32,"raw":"[Xiang et al., 2018] Lei Xiang, Yong Chen, Weitang Chang, Yiqiang Zhan, Weili Lin, Qian Wang, and Dinggang Shen. Deep-learning-based multi-modal fusion for fast MR re- construction. IEEE Transactions on Biomedical Engineer- ing, 66(7):2105-2114, 2018.","title":"Deep-learning-based multi-modal fusion for fast MR reconstruction","authors":"Lei Xiang, Yong Chen, Weitang Chang, Yiqiang Zhan, Weili Lin, Qian Wang, Dinggang Shen","year":"2018","venue":"IEEE Transactions on Biomedical Engineering","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Yang et al., 2018] Yan Yang, Jian Sun, Huibin Li, and Zongben Xu. ADMM-CSNet: A deep learning approach for image compressive sensing. IEEE Transactions on Pat- tern Analysis and Machine Intelligence, 42(3):521-538, 2018.","title":"ADMM-CSNet: A deep learning approach for image compressive sensing","authors":"Yan Yang, Jian Sun, Huibin Li, Zongben Xu","year":"2018","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Zamir et al., 2022] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5728-5739, 2022.","title":"Restormer: Efficient transformer for high-resolution image restoration","authors":"Aditya Syed Waqas Zamir, Salman Arora, Munawar Khan, Fahad Hayat, Ming-Hsuan Shahbaz Khan, Yang","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Zhang et al., 2018] Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu. Image super- resolution using very deep residual channel attention net- works. In Proceedings of the European conference on computer vision (ECCV), pages 286-301, 2018.","title":"Image superresolution using very deep residual channel attention networks","authors":"Zhang","year":"2018","venue":"Proceedings of the European conference on computer vision (ECCV)","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Zhang et al., 2021a] Jinwei Zhang, Hang Zhang, Chao Li, Pascal Spincemaille, Mert Sabuncu, Thanh D Nguyen, and Yi Wang. Temporal feature fusion with sampling pat- tern optimization for multi-echo gradient echo acquisition and image reconstruction. In International Conference on Medical Image Computing and Computer-Assisted Inter- vention, pages 232-242. Springer, 2021.","title":"Temporal feature fusion with sampling pattern optimization for multi-echo gradient echo acquisition and image reconstruction","authors":"Zhang","year":"2021","venue":"International Conference on Medical Image Computing and Computer-Assisted Intervention","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Zhang et al., 2021b] Yulun Zhang, Kai Li, Kunpeng Li, and Yun Fu. MR image super-resolution with squeeze and ex- citation reasoning attention network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 13425-13434, 2021.","title":"MR image super-resolution with squeeze and excitation reasoning attention network","authors":"Zhang","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Zhao et al., 2019] Xiaole Zhao, Yulun Zhang, Tao Zhang, and Xueming Zou. Channel splitting network for single MR image super-resolution. IEEE Transactions on Image Processing, 28(11):5649-5662, 2019.","title":"Channel splitting network for single MR image super-resolution","authors":"Zhao","year":"2019","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Zheng et al., 2019] Hao Zheng, Faming Fang, and Guixu Zhang. Cascaded dilated dense network with two-step data consistency for MRI reconstruction. Advances in Neural Information Processing Systems, 32:1742-1752, 2019.","title":"Cascaded dilated dense network with two-step data consistency for MRI reconstruction","authors":"Faming Hao Zheng, Guixu Fang, Zhang","year":"2019","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}