@inproceedings{ffc648c3a54a431fb9dc95e5448bc7da,
title = "Accelerating magnetic resonance imaging via deep learning",
abstract = "This paper proposes a deep learning approach for accelerating magnetic resonance imaging (MRI) using a large number of existing high quality MR images as the training datasets. An off-line convolutional neural network is designed and trained to identify the mapping relationship between the MR images obtained from zero-filled and fully-sampled k-space data. The network is not only capable of restoring fine structures and details but is also compatible with online constrained reconstruction methods. Experimental results on real MR data have shown encouraging performance of the proposed method for efficient and accurate imaging.",
keywords = "convolutional neural network, Deep learning, magnetic resonance imaging, prior knowledge",
author = "Shanshan Wang and Zhenghang Su and Leslie Ying and Xi Peng and Shun Zhu and Feng Liang and Dagan Feng and Dong Liang",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 ; Conference date: 13-04-2016 Through 16-04-2016",
year = "2016",
month = jun,
day = "15",
doi = "10.1109/ISBI.2016.7493320",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "514--517",
booktitle = "2016 IEEE International Symposium on Biomedical Imaging",
address = "United States",
}