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Abnormal Patterns of Wall Shear Stress in Aortic Dilation Revealed by Permutation Tests
Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-0770-0769
Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering.ORCID iD: 0000-0001-8759-7142
Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences.ORCID iD: 0000-0003-4953-6124
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.ORCID iD: 0000-0001-7061-7995
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2024 (English)In: Journal of Cardiovascular Magnetic Resonance, ISSN 1097-6647, E-ISSN 1532-429X, Vol. 26, article id 100612Article in journal, Meeting abstract (Refereed) Published
Abstract [en]

Four-dimensional flow (4D Flow) CMR affords comprehensive 3D maps of advanced hemodynamics parameters such as wall shear stress (WSS). However, the evaluation of these data is often restricted to spatial averages in large regions of interests, such as the ascending aorta. Recent studies have explored ways of analyzing local intercohort WSS differences by using basic statistical tests with a p-value of 0.05 for determining significance, thus not accounting for the large number of comparisons made when exploring differences for multiple locations across the ascending aorta surface.

Permutation tests, frequently used in brain MRI, permit statistical analysis on a local level while controlling for the family-wise error rate by constructing the null hypothesis distribution based on the maximum statistic over the voxels at each permutation. We sought to use permutation tests to identify local regions of abnormal WSS in the ascending aorta in patients with aortic dilation.

Place, publisher, year, edition, pages
Elsevier, 2024. Vol. 26, article id 100612
Keywords [en]
Aortic Dilation; Wall Shear Stress; magnetic resonance imaging
National Category
Radiology, Nuclear Medicine and Medical Imaging Cardiology and Cardiovascular Disease Medical Imaging
Identifiers
URN: urn:nbn:se:liu:diva-207855DOI: 10.1016/j.jocmr.2024.100612OAI: oai:DiVA.org:liu-207855DiVA, id: diva2:1901281
Available from: 2024-09-26 Created: 2024-09-26 Last updated: 2025-04-22Bibliographically approved

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Trenti, ChiaraBoito, DenebHammaréus, FilipEklund, AndersSwahn, EvaJonasson, LenaWegmann, BertilDyverfeldt, Petter

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Trenti, ChiaraBoito, DenebHammaréus, FilipEklund, AndersSwahn, EvaJonasson, LenaWegmann, BertilDyverfeldt, Petter
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Division of Diagnostics and Specialist MedicineFaculty of Medicine and Health SciencesCenter for Medical Image Science and Visualization (CMIV)Faculty of Science & EngineeringDivision of Biomedical EngineeringThe Division of Statistics and Machine LearningDepartment of Cardiology in LinköpingFaculty of Arts and Sciences
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Journal of Cardiovascular Magnetic Resonance
Radiology, Nuclear Medicine and Medical ImagingCardiology and Cardiovascular DiseaseMedical Imaging

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