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Automated 4D Flow MRI Pipeline for the Quantification of Advanced Hemodynamic Parameters in the Left Atrium

aut.relation.articlenumber5426
aut.relation.issue1
aut.relation.journalScientific Reports
aut.relation.startpage5426
aut.relation.volume16
dc.contributor.authorMorales, Xabier
dc.contributor.authorElsayed, Ayah
dc.contributor.authorZhao, Debbie
dc.contributor.authorLoncaric, Filip
dc.contributor.authorAguado, Ainhoa
dc.contributor.authorMasias, Mireia
dc.contributor.authorQuill, Gina
dc.contributor.authorRamos, Marc
dc.contributor.authorDoltra, Adelina
dc.contributor.authorGarcía-Alvarez, Ana
dc.contributor.authorSitges, Marta
dc.contributor.authorMarlevi, David
dc.contributor.authorYoung, Alistair
dc.contributor.authorNash, Martyn
dc.contributor.authorBijnens, Bart
dc.contributor.authorCamara, Oscar
dc.date.accessioned2026-04-17T03:07:56Z
dc.date.available2026-04-17T03:07:56Z
dc.date.issued2026-01-16
dc.description.abstractThe left atrium (LA) plays a pivotal role in modulating left ventricular filling, yet its hemodynamics remain poorly understood due to the limitations of conventional ultrasound analysis. Four-dimensional flow magnetic resonance imaging (4D Flow MRI) holds promise for enhancing our understanding of atrial hemodynamics, but its analysis is hindered by the inherently low velocities within the chamber and the modest spatial resolution of 4D Flow MRI. Heterogeneity in acquisition protocols and MRI vendors, and the lack of standardized computational frameworks further complicates the creation of large, comparable datasets needed to assess the prognostic value of hemodynamic markers provided by 4D Flow MRI. To address these challenges, we introduce a computational framework tailored to the analysis of 4D Flow MRI in the LA, enabling the qualitative and quantitative analysis of advanced hemodynamic parameters (e.g., kinetic energy, vorticity, and pressure). We applied this framework to a diverse cohort spanning different degrees of left ventricular diastolic dysfunction to investigate the prognostic potential of these metrics. Our framework proved robustness across multicenter data of varying quality, producing high-accuracy automated segmentations. Notably, our findings show that 4D Flow MRI-derived parameters provide superior differentiation between healthy and pathological states than those available to conventional hemodynamic analysis tools.
dc.identifier.citationScientific Reports, ISSN: 2045-2322 (Print); 2045-2322 (Online), Nature Portfolio, 16(1), 5426-. doi: 10.1038/s41598-025-34972-7
dc.identifier.doi10.1038/s41598-025-34972-7
dc.identifier.issn2045-2322
dc.identifier.issn2045-2322
dc.identifier.urihttp://hdl.handle.net/10292/20946
dc.languageeng
dc.publisherNature Portfolio
dc.relation.urihttps://www.nature.com/articles/s41598-025-34972-7
dc.rightsOpen Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.accessrightsOpenAccess
dc.subject4D flow MRI
dc.subjectComputational pipeline
dc.subjectDeep learning
dc.subjectHemodynamics
dc.subjectLeft atrium
dc.subjectLeft ventricular diastolic dysfunction
dc.subject4D flow MRI
dc.subjectComputational pipeline
dc.subjectDeep learning
dc.subjectHemodynamics
dc.subjectLeft atrium
dc.subjectLeft ventricular diastolic dysfunction
dc.subject4012 Fluid Mechanics and Thermal Engineering
dc.subject32 Biomedical and Clinical Sciences
dc.subject40 Engineering
dc.subject4003 Biomedical Engineering
dc.subjectBiomedical Imaging
dc.subjectBioengineering
dc.subjectNetworking and Information Technology R&D (NITRD)
dc.subjectCardiovascular
dc.subject.meshHumans
dc.subject.meshHeart Atria
dc.subject.meshHemodynamics
dc.subject.meshMagnetic Resonance Imaging
dc.subject.meshMale
dc.subject.meshFemale
dc.subject.meshMiddle Aged
dc.subject.meshAged
dc.subject.meshVentricular Dysfunction, Left
dc.subject.meshHeart Atria
dc.subject.meshHumans
dc.subject.meshVentricular Dysfunction, Left
dc.subject.meshMagnetic Resonance Imaging
dc.subject.meshAged
dc.subject.meshMiddle Aged
dc.subject.meshFemale
dc.subject.meshMale
dc.subject.meshHemodynamics
dc.subject.meshHumans
dc.subject.meshHeart Atria
dc.subject.meshHemodynamics
dc.subject.meshMagnetic Resonance Imaging
dc.subject.meshMale
dc.subject.meshFemale
dc.subject.meshMiddle Aged
dc.subject.meshAged
dc.subject.meshVentricular Dysfunction, Left
dc.titleAutomated 4D Flow MRI Pipeline for the Quantification of Advanced Hemodynamic Parameters in the Left Atrium
dc.typeJournal Article
pubs.elements-id751395

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