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Conference Paper: CU-Net: A Completely Complex U-Net for MR k-space Signal Processing
Title | CU-Net: A Completely Complex U-Net for MR k-space Signal Processing |
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Authors | |
Issue Date | 2021 |
Publisher | Internationala 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. 2618 How to Cite? |
Abstract | While the application of deep learning in MR image analysis has gained significant popularity, using raw MR k-space data as part of deep learning analysis is an underexplored area. Here we develop a completely complex U-Net deep learning architecture, CU-Net, where we apply deep learning components and operations in the complex space. CU-Net leverages k-space MR signals while training a U-Net with Attention and Residual components, as opposed to using processed spatial (real) data, typically seen with MRI deep learning applications. As part of a proof-of-concept study, the complex networks demonstrated their utility and potential superiority over their spatial counterparts. |
Description | Digital Posters Session D-112: Optimized Signal Representation for Acquisition & Reconstruction - no. 2618 |
Persistent Identifier | http://hdl.handle.net/10722/304067 |
DC Field | Value | Language |
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dc.contributor.author | Sikka, D | - |
dc.contributor.author | Igra, N | - |
dc.contributor.author | Gjerwold-Sellec, S | - |
dc.contributor.author | Gao, C | - |
dc.contributor.author | Wu, EX | - |
dc.contributor.author | Guo, J | - |
dc.date.accessioned | 2021-09-23T08:54:47Z | - |
dc.date.available | 2021-09-23T08:54:47Z | - |
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. 2618 | - |
dc.identifier.uri | http://hdl.handle.net/10722/304067 | - |
dc.description | Digital Posters Session D-112: Optimized Signal Representation for Acquisition & Reconstruction - no. 2618 | - |
dc.description.abstract | While the application of deep learning in MR image analysis has gained significant popularity, using raw MR k-space data as part of deep learning analysis is an underexplored area. Here we develop a completely complex U-Net deep learning architecture, CU-Net, where we apply deep learning components and operations in the complex space. CU-Net leverages k-space MR signals while training a U-Net with Attention and Residual components, as opposed to using processed spatial (real) data, typically seen with MRI deep learning applications. As part of a proof-of-concept study, the complex networks demonstrated their utility and potential superiority over their spatial counterparts. | - |
dc.language | eng | - |
dc.publisher | Internationala Society of Magnetic Resonance Imaging (ISMRM) . | - |
dc.relation.ispartof | ISMRM (International Society of Magnetic Resonance Imaging) Virtual Conference & Exhibition, 2021 | - |
dc.title | CU-Net: A Completely Complex U-Net for MR k-space Signal Processing | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Wu, EX: ewu@eee.hku.hk | - |
dc.identifier.authority | Wu, EX=rp00193 | - |
dc.identifier.hkuros | 325468 | - |
dc.identifier.spage | 2618 | - |
dc.identifier.epage | 2618 | - |