Unsupervised Learning-based Pulse Sequence Optimization framework for Magnetic Resonance Fingerprinting

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dc.cclicenceCC-BY-NCen
dc.contributor.authorLi, Peng
dc.contributor.authorZhang, Yinghao
dc.contributor.authorLu, Xin
dc.contributor.authorHu, Yue
dc.date.acceptance2023-02-15
dc.date.accessioned2023-02-22T11:31:07Z
dc.date.available2023-02-22T11:31:07Z
dc.date.issued2023-02-13
dc.description.abstractThe optimal design of the Magnetic resonance fingerprinting (MRF) sequence is still challenging due to the optimization of high-degrees-of-freedom acquisition parameters. In this paper, we propose a novel unsupervised learning-based pulse sequence design framework for efficient MRF sequence optimization. Specifically, we propose a novel pulse sequence generation network (PSG-Net) that fully exploits the sequence correlation to generate the optimal pulse sequence from a zero-initialized input. To achieve improved precision of parameter estimation, we use a predefined pulse sequence performance evaluation function that can directly represent tissue quantification separability as the loss function to update the parameters of the PSG-Net.en
dc.funderOther external funder (please detail below)en
dc.funder.otherNature Science Foundation of Chinaen
dc.funder.otherNatural Science Foundation of Heilongjiangen
dc.identifier.citationLi, P., Zhang, Y., Lu, X. and Hu, Y. (2023) Unsupervised Learning-based Pulse Sequence Optimization framework for Magnetic Resonance Fingerprinting. 2023 ISMRM & ISMRT Annual Meeting & Exhibition, Toronto, Canada, 03-08 Juneen
dc.identifier.urihttps://hdl.handle.net/2086/22519
dc.language.isoenen
dc.peerreviewedYesen
dc.projectidNSFC 61871159en
dc.projectidYQ2021F005en
dc.publisherISMRM & ISMRTen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectPulse Sequence Designen
dc.subjectMR Fingerprintingen
dc.titleUnsupervised Learning-based Pulse Sequence Optimization framework for Magnetic Resonance Fingerprintingen
dc.typeConferenceen

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