Conference proceeding
Accelerating magnetic resonance imaging via deep learning
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Vol.2016-, pp.514-517
04/01/2016
DOI: 10.1109/ISBI.2016.7493320
PMCID: PMC6839781
PMID: 31709031
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.
Details
- Title: Subtitle
- Accelerating magnetic resonance imaging via deep learning
- Creators
- Shanshan Wang - Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, CAS, Shenzhen, P.R. ChinaZhenghang Su - Guangdong University of TechnologyLeslie Ying - State University of New YorkXi Peng - Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, CAS, Shenzhen, P.R. ChinaShun Zhu - Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, CAS, Shenzhen, P.R. ChinaFeng Liang - Nankai UniversityDagan Feng - University of SydneyDong Liang - Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, CAS, Shenzhen, P.R. China
- Resource Type
- Conference proceeding
- Publication Details
- 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Vol.2016-, pp.514-517
- Publisher
- IEEE
- DOI
- 10.1109/ISBI.2016.7493320
- PMID
- 31709031
- PMCID
- PMC6839781
- ISSN
- 1945-7928
- eISSN
- 1945-8452
- Language
- English
- Date published
- 04/01/2016
- Academic Unit
- Radiology
- Record Identifier
- 9984446515402771
Metrics
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