Self-supervised motion-corrected image reconstruction network for 4D magnetic resonance imaging of the body trunk

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dc.article.numbere12
dc.catalogadorpva
dc.contributor.authorKüstner, Thomas
dc.contributor.authorPan, Jiazhen
dc.contributor.authorGilliam, Christopher
dc.contributor.authorQi, Haikun
dc.contributor.authorCruz, Gastao
dc.contributor.authorHammernik, Kerstin
dc.contributor.authorBlu, Thierry
dc.contributor.authorRueckert, Daniel
dc.contributor.authorBotnar, René Michael
dc.contributor.authorPrieto Vásquez, Claudia
dc.contributor.authorGatidis, Sergios
dc.date.accessioned2024-01-24T16:57:30Z
dc.date.available2024-01-24T16:57:30Z
dc.date.issued2022
dc.description.abstractRespiratory motion can cause artifacts in magnetic resonance imaging of the body trunk if patients cannot hold their breath or triggered acquisitions are not practical. Retrospective correction strategies usually cope with motion by fast imaging sequences under free-movement conditions followed by motion binning based on motion traces. These acquisitions yield sub-Nyquist sampled and motion-resolved k-space data. Motion states are linked to each other by non-rigid deformation fields. Usually, motion registration is formulated in image space which can however be impaired by aliasing artifacts or by estimation from low-resolution images. Subsequently, any motion-corrected reconstruction can be biased by errors in the deformation fields. In this work, we propose a deep-learning based motion-corrected 4D (3D spatial + time) image reconstruction which combines a non-rigid registration network and a 4D reconstruction network. Non-rigid motion is estimated in k-space and incorporated into the reconstruction network. The proposed method is evaluated on in-vivo 4D motion-resolved magnetic resonance images of patients with suspected liver or lung metastases and healthy subjects. The proposed approach provides 4D motion-corrected images and deformation fields. It enables a ∼ 14× accelerated acquisition with a 25- fold faster reconstruction than comparable approaches under consistent preservation of image quality for changing patients and motion patterns.
dc.description.funderEPSRC
dc.description.funderDeutsche Forschungsgemeinschaft
dc.fechaingreso.objetodigital2024-01-24
dc.fuente.origenScopus
dc.identifier.doi10.1561/116.00000039
dc.identifier.eissn2048-7703
dc.identifier.scopusidSCOPUS_ID:85130081305
dc.identifier.urihttp://dx.doi.org/10.1561/116.00000039
dc.identifier.urihttps://www.nowpublishers.com/SIP
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/80940
dc.identifier.wosidWOS:000916447600009
dc.information.autorucEscuela de Ingeniería; Botnar, René Michael; 0000-0002-9447-4367; 1015313
dc.information.autorucEscuela de Ingeniería; Prieto Vásquez, Claudia; 0000-0003-4602-2523; 14195
dc.issue.numero1
dc.language.isoen
dc.nota.accesoContenido completo
dc.pagina.final27
dc.pagina.inicio1
dc.publisherNow Publishers Inc
dc.revistaAPSIPA Transactions on Signal and Information Processinges_ES
dc.rightsacceso abierto
dc.rights.licenseCC BY-NC 4.0 DEED Attribution-NonCommercial 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectMotion-compensated image reconstructiones_ES
dc.subjectMagnetic resonance imaginges_ES
dc.subjectImage registrationes_ES
dc.subjectDeep learning reconstructiones_ES
dc.subject.ddc620
dc.subject.deweyIngenieríaes_ES
dc.subject.ods03 Good health and well-being
dc.subject.odspa03 Salud y bienestar
dc.titleSelf-supervised motion-corrected image reconstruction network for 4D magnetic resonance imaging of the body trunkes_ES
dc.typeartículo
dc.volumen11
sipa.codpersvinculados1015313
sipa.codpersvinculados14195
sipa.trazabilidadSCOPUS;2022-07-08
sipa.trazabilidadORCID;2024-01-15
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