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Conference Paper: Deep Learning Image Reconstruction from Incomplete Fast Spin Echo MR Data
Title | Deep Learning Image Reconstruction from Incomplete Fast Spin Echo MR Data |
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Authors | |
Issue Date | 2021 |
Publisher | International Society of Magnetic Resonance Imaging (ISMRM) . |
Citation | Proceedings of the 29th Annual Meeting & Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), Virtual Conference, Vancouver, BC, Canada, 15-20 May 2021, paper no. 1963 How to Cite? |
Abstract | Fast spin echo (FSE) is the most commonly used multi-shot sequence in clinical MRI. In this study, we propose to acquire single-channel FSE data with incomplete number of shots (TRs), and reconstruct such periodically undersampled k-space data using a deep learning approach. The results demonstrate that the proposed method can effectively remove the aliasing artifacts and recover the high frequency information without noise amplification, enabling a FSE acceleration that can be readily implemented in practice. |
Description | Digital Posters Session D-92: Machine Learning for Image Reconstruction - no. 1963 |
Persistent Identifier | http://hdl.handle.net/10722/304351 |
DC Field | Value | Language |
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dc.contributor.author | Xiao, L | - |
dc.contributor.author | Liu, Y | - |
dc.contributor.author | Zhao, Y | - |
dc.contributor.author | Yi, Z | - |
dc.contributor.author | Lau, V | - |
dc.contributor.author | Leong, TL | - |
dc.contributor.author | Wu, EX | - |
dc.date.accessioned | 2021-09-23T08:58:50Z | - |
dc.date.available | 2021-09-23T08:58:50Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Proceedings of the 29th Annual Meeting & Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), Virtual Conference, Vancouver, BC, Canada, 15-20 May 2021, paper no. 1963 | - |
dc.identifier.uri | http://hdl.handle.net/10722/304351 | - |
dc.description | Digital Posters Session D-92: Machine Learning for Image Reconstruction - no. 1963 | - |
dc.description.abstract | Fast spin echo (FSE) is the most commonly used multi-shot sequence in clinical MRI. In this study, we propose to acquire single-channel FSE data with incomplete number of shots (TRs), and reconstruct such periodically undersampled k-space data using a deep learning approach. The results demonstrate that the proposed method can effectively remove the aliasing artifacts and recover the high frequency information without noise amplification, enabling a FSE acceleration that can be readily implemented in practice. | - |
dc.language | eng | - |
dc.publisher | International Society of Magnetic Resonance Imaging (ISMRM) . | - |
dc.relation.ispartof | ISMRM (International Society of Magnetic Resonance Imaging) Virtual Conference & Exhibition, 2021 | - |
dc.title | Deep Learning Image Reconstruction from Incomplete Fast Spin Echo MR Data | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Leong, TL: tlleong@hku.hk | - |
dc.identifier.email | Wu, EX: ewu@eee.hku.hk | - |
dc.identifier.authority | Leong, TL=rp02483 | - |
dc.identifier.authority | Wu, EX=rp00193 | - |
dc.identifier.hkuros | 325460 | - |
dc.identifier.spage | 1963 | - |
dc.identifier.epage | 1963 | - |