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Conference Paper: Jointly Denoise Diffusion-weighted Images Using a Weighted Nuclear Norm Minimization Approach
Title | Jointly Denoise Diffusion-weighted Images Using a Weighted Nuclear Norm Minimization Approach |
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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. 1334 How to Cite? |
Abstract | Diffusion MRI intrinsically suffers from low signal-to-noise ratio (SNR), especially when spatial resolution or b-value is high. A typical diffusion MRI scanning session produces image sets with same geometries but different diffusion directions and b-values, thus these diffusion-weighted (DW) images often share strong structural similarities. In this study, we developed a joint denoising method for DW images based on low-rank matrix approximation. This denoising method exploits structural similarities of DW image set. Both simulation and in vivo brain experiments demonstrate significant noise reduction in all DW images, revealing more microstructural details in quantitative diffusion maps. |
Description | Digital Posters Session D-140: Diffusion Acquisition & Post-Processing - no. 1334 |
Persistent Identifier | http://hdl.handle.net/10722/304066 |
DC Field | Value | Language |
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dc.contributor.author | Zhao, Y | - |
dc.contributor.author | Xiao, L | - |
dc.contributor.author | Zhang, Z | - |
dc.contributor.author | Liu, Y | - |
dc.contributor.author | Guo, H | - |
dc.contributor.author | Wu, EX | - |
dc.date.accessioned | 2021-09-23T08:54:46Z | - |
dc.date.available | 2021-09-23T08:54:46Z | - |
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. 1334 | - |
dc.identifier.uri | http://hdl.handle.net/10722/304066 | - |
dc.description | Digital Posters Session D-140: Diffusion Acquisition & Post-Processing - no. 1334 | - |
dc.description.abstract | Diffusion MRI intrinsically suffers from low signal-to-noise ratio (SNR), especially when spatial resolution or b-value is high. A typical diffusion MRI scanning session produces image sets with same geometries but different diffusion directions and b-values, thus these diffusion-weighted (DW) images often share strong structural similarities. In this study, we developed a joint denoising method for DW images based on low-rank matrix approximation. This denoising method exploits structural similarities of DW image set. Both simulation and in vivo brain experiments demonstrate significant noise reduction in all DW images, revealing more microstructural details in quantitative diffusion maps. | - |
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 | Jointly Denoise Diffusion-weighted Images Using a Weighted Nuclear Norm Minimization Approach | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Wu, EX: ewu@eee.hku.hk | - |
dc.identifier.authority | Wu, EX=rp00193 | - |
dc.identifier.hkuros | 325457 | - |
dc.identifier.spage | 1334 | - |
dc.identifier.epage | 1334 | - |