DC Field | Value | Language |
---|---|---|
dc.contributor.author | Luu, Huan Minh | ko |
dc.contributor.author | Park, Sung-Hong | ko |
dc.date.accessioned | 2023-12-28T03:00:34Z | - |
dc.date.available | 2023-12-28T03:00:34Z | - |
dc.date.created | 2023-12-27 | - |
dc.date.issued | 2023-06-07 | - |
dc.identifier.citation | 2023 ISMRM & ISMRT Annual Meeting & Exhibition , pp.912 | - |
dc.identifier.uri | http://hdl.handle.net/10203/316962 | - |
dc.description.abstract | Most quantitative magnetization transfer imaging (qMT) protocols require additional T1 mapping scan. A recent on-resonance multiple phase-cycle bSSFP method was proposed for qMT that obviates the necessity for T1 mapping, but the fitting results were suboptimal. In this study, we proposed a physics-informed artificial neural network (ANN) to improve the fitting of this method. By using the MR signal model to generate the training data and regularize the network, no in-vivo data acquisition was necessary. Experiments on digital phantom and in-vivo data demonstrated improvement over previous method and better resilience against measurement noise. | - |
dc.language | English | - |
dc.publisher | International Society for Magnetic Resonance in Medicine | - |
dc.title | Improving bSSFP-based quantitative magnetization transfer imaging with MR physics-informed artificial neural network | - |
dc.type | Conference | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 912 | - |
dc.citation.publicationname | 2023 ISMRM & ISMRT Annual Meeting & Exhibition | - |
dc.identifier.conferencecountry | CN | - |
dc.identifier.conferencelocation | Toronto | - |
dc.contributor.localauthor | Park, Sung-Hong | - |
dc.contributor.nonIdAuthor | Luu, Huan Minh | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.