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A deep learning image analysis method for renal perfusion estimation in pseudo-continuous arterial spin labelling MRI

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dc.contributor.authorOyarzun Domeño, Anne
dc.contributor.authorCía Alonso, Izaskun
dc.contributor.authorEcheverría Chasco, Rebeca
dc.contributor.authorFernández Seara, María A.
dc.contributor.authorMartín Moreno, Paloma L.
dc.contributor.authorBastarrika, Gorka
dc.contributor.authorNavallas Irujo, Javier
dc.contributor.authorVillanueva Larre, Arantxa
dc.contributor.departmentIngeniería Eléctrica, Electrónica y de Comunicaciónes_ES
dc.contributor.departmentInstitute of Smart Cities - ISCen
dc.contributor.departmentIngeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzareneu
dc.contributor.funderUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoaes
dc.date.accessioned2024-04-25T13:30:26Z
dc.date.available2024-04-25T13:30:26Z
dc.date.issued2023
dc.date.updated2024-04-25T13:01:54Z
dc.description.abstractAccurate segmentation of renal tissues is an essential step for renal perfusion estimation and postoperative assessment of the allograft. Images are usually manually labeled, which is tedious and prone to human error. We present an image analysis method for the automatic estimation of renal perfusion based on perfusion magnetic resonance imaging. Specifically, non-contrasted pseudo-continuous arterial spin labeling (PCASL) images are used for kidney transplant evaluation and perfusion estimation, as a biomarker of the status of the allograft. The proposed method uses machine/deep learning tools for the segmentation and classification of renal cortical and medullary tissues and automates the estimation of perfusion values. Data from 16 transplant patients has been used for the experiments. The automatic analysis of differentiated tissues within the kidney, such as cortex and medulla, is performed by employing the time-intensity-curves of non-contrasted T1-weighted MRI series. Specifically, using the Dice similarity coefficient as a figure of merit, results above 93%, 92% and 82% are obtained for whole kidney, cortex, and medulla, respectively. Besides, estimated cortical and medullary perfusion values are considered to be within the acceptable ranges within clinical practice.en
dc.description.sponsorshipProject PC181-182 RM-RENAL, supported by the Department of University, Innovation and Digital Transformation (Government of Navarre). The author would also like to acknowledge the Department of University, Innovation and Digital Transformation (Government of Navarre) for the predoctoral grant number 0011-0537-2021-000050. Open access funding provided by Universidad Pública de Navarra.en
dc.format.mimetypeapplication/pdfen
dc.identifier.citationOyarzun-Domeño, A., Cia, I., Echeverria-Chasco, R., Fernández-Seara, M. A., Martin-Moreno, P. L., Garcia-Fernandez, N., Bastarrika, G., Navallas, J., Villanueva, A. (2023) A deep learning image analysis method for renal perfusion estimation in pseudo-continuous arterial spin labelling MRI. Magnetic Resonance Imaging, 104, 39-51. https://doi.org/10.1016/j.mri.2023.09.007.en
dc.identifier.doi10.1016/j.mri.2023.09.007
dc.identifier.issn0730-725X
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/48036
dc.language.isoengen
dc.publisherElsevieren
dc.relation.ispartofMagnetic Resonance Imaging 104, (2023), 39–51en
dc.relation.projectIDinfo:eu-repo/grantAgreement/Gobierno de Navarra//PC181-182 RM-RENALen
dc.relation.projectIDinfo:eu-repo/grantAgreement/Gobierno de Navarra//0011-0537-2021-000050en
dc.relation.publisherversionhttps://doi.org/10.1016/j.mri.2023.09.007
dc.rights© 2023 The Authors. This is an open access article under the CC BY-NC license.en
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/bync/4.0/
dc.subjectAllograften
dc.subjectDeep learningen
dc.subjectMRIen
dc.subjectRenal perfusionen
dc.subjectSegmentationen
dc.titleA deep learning image analysis method for renal perfusion estimation in pseudo-continuous arterial spin labelling MRIen
dc.typeinfo:eu-repo/semantics/article
dc.type.versionVersión publicada / Argitaratu den bertsioaes
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dspace.entity.typePublication
relation.isAuthorOfPublication5cfc3bb5-70a4-49da-9dcb-ea6cd22e0780
relation.isAuthorOfPublication9650a6c3-5f76-4005-9979-c140061b5e3c
relation.isAuthorOfPublicationd3bfd5bf-8426-455b-bcc4-897ddb0d4c2e
relation.isAuthorOfPublication.latestForDiscovery9650a6c3-5f76-4005-9979-c140061b5e3c

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