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Conference Paper: Joint Calibrationless Reconstruction of Highly Undersampled Multi-Contrast MR Datasets Using A Novel Low-Rank Completion Approach
Title | Joint Calibrationless Reconstruction of Highly Undersampled Multi-Contrast MR Datasets Using A Novel Low-Rank Completion Approach |
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Authors | |
Issue Date | 2019 |
Publisher | International Society for Magnetic Resonance in Medicine. |
Citation | The 27th Annual Meeting & Exhibition of International Society for Magnetic Resonance in Medicine (ISMRM), Montreal, Canada, 11-16 May 2019 , article no. 4746 How to Cite? |
Abstract | Routine clinical MRI session often requires multi-contrast imaging with identical geometries but different contrasts, and these images of different contrasts are independently reconstructed despite ubiquitous similarities. Simultaneous autocalibrating and k-space estimation (SAKE) provides a powerful calibrationless parallel imaging approach to reduce scanning time through undersampling. However, traditional SAKE reconstruction does not utilize redundant information embedded in multi-contrast datasets. In this study, we propose to advance SAKE by jointly reconstructing concatenated multi-contrast datasets using a novel low-rank completion approach. Our new method explicitly exploits the correlations in multi-contrast datasets and outperforms the traditional SAKE, leading to higher acceleration factors. |
Description | Digital Poster Session: Acquisition, Reconstruction & Analysis - Image Reconstruction I - no. 4746 |
Persistent Identifier | http://hdl.handle.net/10722/278725 |
DC Field | Value | Language |
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dc.contributor.author | Yi, Z | - |
dc.contributor.author | Liu, Y | - |
dc.contributor.author | Zhao, Y | - |
dc.contributor.author | Chen, F | - |
dc.contributor.author | Wu, EX | - |
dc.date.accessioned | 2019-10-21T02:12:52Z | - |
dc.date.available | 2019-10-21T02:12:52Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | The 27th Annual Meeting & Exhibition of International Society for Magnetic Resonance in Medicine (ISMRM), Montreal, Canada, 11-16 May 2019 , article no. 4746 | - |
dc.identifier.uri | http://hdl.handle.net/10722/278725 | - |
dc.description | Digital Poster Session: Acquisition, Reconstruction & Analysis - Image Reconstruction I - no. 4746 | - |
dc.description.abstract | Routine clinical MRI session often requires multi-contrast imaging with identical geometries but different contrasts, and these images of different contrasts are independently reconstructed despite ubiquitous similarities. Simultaneous autocalibrating and k-space estimation (SAKE) provides a powerful calibrationless parallel imaging approach to reduce scanning time through undersampling. However, traditional SAKE reconstruction does not utilize redundant information embedded in multi-contrast datasets. In this study, we propose to advance SAKE by jointly reconstructing concatenated multi-contrast datasets using a novel low-rank completion approach. Our new method explicitly exploits the correlations in multi-contrast datasets and outperforms the traditional SAKE, leading to higher acceleration factors. | - |
dc.language | eng | - |
dc.publisher | International Society for Magnetic Resonance in Medicine. | - |
dc.relation.ispartof | ISMRM (International Society for Magnetic Resonance in Medicine) 27th Annual Meeting, 2019 | - |
dc.title | Joint Calibrationless Reconstruction of Highly Undersampled Multi-Contrast MR Datasets Using A Novel Low-Rank Completion Approach | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Wu, EX: ewu@eee.hku.hk | - |
dc.identifier.authority | Wu, EX=rp00193 | - |
dc.identifier.hkuros | 307715 | - |
dc.identifier.hkuros | 304419 | - |
dc.identifier.spage | p4746 | - |
dc.identifier.epage | p4746 | - |
dc.publisher.place | United States | - |