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Conference Paper: Synthesize Quantitative Susceptibility Mapping from Susceptibility Weighting Imaging Using a Cycle Generative Adversarial Network
Title | Synthesize Quantitative Susceptibility Mapping from Susceptibility Weighting Imaging Using a Cycle Generative Adversarial Network |
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Authors | |
Issue Date | 2021 |
Publisher | International Society for Magnetic Resonance in Medicine (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. 3257 How to Cite? |
Abstract | Quantitative susceptibility mapping (QSM) obtained from the MRI phase images is valuable in neurological disease diagnoses. Meanwhile, the role of thumb MRI scan probing susceptibility contrast is susceptibility weighting imaging (SWI), which might contain blooming artifacts that would affect the hypointensity appearance. Many conventional methods have been developed for QSM reconstruction, including the deep learning-based approach that is applicable in clinical diagnoses. Here, we apply the cycle generative adversarial network with a perceptual loss to synthesize QSM images from SWI images. The predicted QSM images showed their application in brain microbleed detection. |
Description | Session Number: D-68 Digital Posters - New Frontiers of AI in Neuroimaging - no. 3257 |
Persistent Identifier | http://hdl.handle.net/10722/305964 |
DC Field | Value | Language |
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dc.contributor.author | Wang, Z | - |
dc.contributor.author | Xia, P | - |
dc.contributor.author | Mak, HKF | - |
dc.contributor.author | Cao, P | - |
dc.date.accessioned | 2021-10-20T10:16:53Z | - |
dc.date.available | 2021-10-20T10:16:53Z | - |
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. 3257 | - |
dc.identifier.uri | http://hdl.handle.net/10722/305964 | - |
dc.description | Session Number: D-68 Digital Posters - New Frontiers of AI in Neuroimaging - no. 3257 | - |
dc.description.abstract | Quantitative susceptibility mapping (QSM) obtained from the MRI phase images is valuable in neurological disease diagnoses. Meanwhile, the role of thumb MRI scan probing susceptibility contrast is susceptibility weighting imaging (SWI), which might contain blooming artifacts that would affect the hypointensity appearance. Many conventional methods have been developed for QSM reconstruction, including the deep learning-based approach that is applicable in clinical diagnoses. Here, we apply the cycle generative adversarial network with a perceptual loss to synthesize QSM images from SWI images. The predicted QSM images showed their application in brain microbleed detection. | - |
dc.language | eng | - |
dc.publisher | International Society for Magnetic Resonance in Medicine (ISMRM). | - |
dc.relation.ispartof | ISMRM (International Society of Magnetic Resonance Imaging) Virtual Conference & Exhibition, 2021 | - |
dc.title | Synthesize Quantitative Susceptibility Mapping from Susceptibility Weighting Imaging Using a Cycle Generative Adversarial Network | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Mak, HKF: makkf@hku.hk | - |
dc.identifier.email | Cao, P: caopeng1@hku.hk | - |
dc.identifier.authority | Mak, HKF=rp00533 | - |
dc.identifier.authority | Cao, P=rp02474 | - |
dc.identifier.hkuros | 326794 | - |
dc.identifier.spage | 3257 | - |
dc.identifier.epage | 3257 | - |