Unfolded Low-rank + Sparse Reconstruction for MRI

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but.event.date26.04.2022cs
but.event.titleSTUDENT EEICT 2022cs
dc.contributor.authorMokrý, Ondřej
dc.contributor.authorVitouš, Jiří
dc.date.accessioned2022-12-06T13:22:00Z
dc.date.available2022-12-06T13:22:00Z
dc.date.issued2022cs
dc.description.abstractWe apply the methodology of deep unfolding on the problem of reconstruction of DCE-MRI data. The problem is formulated as a convex optimization problem, solvable via the primal–dual splitting algorithm. The unfolding allows for optimal hyperparameter selection for the model. We examine two approaches – with the parameters shared across the layers/iterations, and an adaptive version where the parameters can differ. The results demonstrate that the more complex model can better adapt to the data.en
dc.formattextcs
dc.format.extent271-275cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings II of the 28st Conference STUDENT EEICT 2022: Selected papers. s. 271-275. ISBN 978-80-214-6030-0cs
dc.identifier.doi10.13164/eeict.2022.271
dc.identifier.isbn978-80-214-6030-0
dc.identifier.urihttp://hdl.handle.net/11012/208650
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings II of the 28st Conference STUDENT EEICT 2022: Selected papersen
dc.relation.urihttps://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazenics
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectDCE-MRI, proximal splitting algorithms, deep unfolding, L+S modelen
dc.titleUnfolded Low-rank + Sparse Reconstruction for MRIen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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