dc.contributor.author | Groun, Nourelhouda | |
dc.contributor.author | Villalba-Orero, María | |
dc.contributor.author | Lara-Pezzi, Enrique | |
dc.contributor.author | Valero, Eusebio | |
dc.contributor.author | Garicano-Mena, Jesús | |
dc.contributor.author | Le Clainche, Soledad | |
dc.date.accessioned | 2023-04-27T13:13:08Z | |
dc.date.available | 2023-04-27T13:13:08Z | |
dc.date.issued | 2022-12 | |
dc.identifier.citation | Comput Biol Med. 2022 Dec;151(Pt B):106317. | es_ES |
dc.identifier.uri | http://hdl.handle.net/20.500.12105/15930 | |
dc.description.abstract | Cardiac cine magnetic resonance imaging (MRI) can be considered the optimal criterion for measuring cardiac function. This imaging technique can provide us with detailed information about cardiac structure, tissue composition and even blood flow, which makes it highly used in medical science. But due to the image time acquisition and several other factors the MRI sequences can easily get corrupted, causing radiologists to misdiagnose 40 million people worldwide each and every single year. Hence, the urge to decrease these numbers, researchers from different fields have been introducing novel tools and methods in the medical field. Aiming to the same target, we consider in this work the application of the higher order dynamic mode decomposition (HODMD) technique. The HODMD algorithm is a linear method, which was originally introduced in the fluid dynamics domain, for the analysis of complex systems. Nevertheless, the proposed method has extended its applicability to numerous domains, including medicine. In this work, HODMD in used to analyze sets of MR images of a heart, with the ultimate goal of identifying the main patterns and frequencies driving the heart dynamics. Furthermore, a novel interpolation algorithm based on singular value decomposition combined with HODMD is introduced, providing a three-dimensional reconstruction of the heart. This algorithm is applied (i) to reconstruct corrupted or missing images, and (ii) to build a reduced order model of the heart dynamics. | es_ES |
dc.description.sponsorship | This work has been supported by SIMOPAIR
(Project No. REF: RTI2018-097075-B-I00) funded
by MCIN/AEI/10.13039/501100011033 and by the European Union’s Horizon 2020 research and innovation
program under the Marie Sklodowska-Curie Agreement
number 101019137— FLOWCID.
S.L.C. acknowledges the grant PID2020-114173RB-I00
funded by MCIN/AEI/10.13039/501100011033.
”Biomedical Imaging has been conducted at the Advanced Imaging Unit of the CNIC (Centro Nacional de
Investigaciones Cardiovasculares Carlos III), Madrid,
Spain.” ”This project used the ReDIB ICTS infrastructure TRIMA@CNIC, Ministerio de Ciencia e Innovación
(MCIN).” | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.type.hasVersion | VoR | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject.mesh | Magnetic Resonance Imaging, Cine | es_ES |
dc.subject.mesh | Heart | es_ES |
dc.subject.mesh | Humans | es_ES |
dc.subject.mesh | Algorithms | es_ES |
dc.subject.mesh | Magnetic Resonance Imaging | es_ES |
dc.subject.mesh | Image Processing, Computer-Assisted | es_ES |
dc.title | A novel data-driven method for the analysis and reconstruction of cardiac cine MRI. | es_ES |
dc.type | journal article | es_ES |
dc.rights.license | Atribución 4.0 Internacional | * |
dc.identifier.pubmedID | 36442273 | es_ES |
dc.format.volume | 151 | es_ES |
dc.format.number | Pt B | es_ES |
dc.format.page | 106317 | es_ES |
dc.identifier.doi | 10.1016/j.compbiomed.2022.106317 | es_ES |
dc.contributor.funder | Ministerio de Ciencia e Innovación (España) | es_ES |
dc.contributor.funder | Marie Curie | es_ES |
dc.description.peerreviewed | Sí | es_ES |
dc.identifier.e-issn | 1879-0534 | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.compbiomed.2022.106317 | es_ES |
dc.identifier.journal | Computers in biology and medicine | es_ES |
dc.repisalud.orgCNIC | CNIC::Grupos de investigación::Regulación Molecular de la Insuficiencia Cardiaca | es_ES |
dc.repisalud.institucion | CNIC | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/ES/RTI2018-097075-B-I00 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/ES/PID2020-114173RB-I00 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/ES/MCIN/AEI/10.13039/501100011033 | es_ES |
dc.rights.accessRights | open access | es_ES |
dc.relation.projectFECYT | info:eu-repo/grantAgreement/EC/H2020/101019137 | es_ES |