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4D Flow MRI-Based Pressure Loss Estimation in Stenotic Flows: Evaluation Using Numerical Simulations
Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences.
Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences.ORCID iD: 0000-0003-1942-7699
Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Faculty of Science & Engineering. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping.ORCID iD: 0000-0003-1395-8296
2016 (English)In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 75, no 4, p. 1808-1821Article in journal (Refereed) Published
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Text
Abstract [en]

Purpose: To assess how 4D flow MRI-based pressure and energy loss estimates correspond to net transstenotic pressure gradients (TPG(net)) and their dependence on spatial resolution. Methods: Numerical velocity data of stenotic flow were obtained from computational fluid dynamics (CFD) simulations in geometries with varying stenosis degrees, poststenotic diameters and flow rates. MRI measurements were simulated at different spatial resolutions. The simplified and extended Bernoulli equations, Pressure-Poisson equation (PPE), and integration of turbulent kinetic energy (TKE) and viscous dissipation were compared against the true TPG(net). Results: The simplified Bernoulli equation overestimated the true TPG(net) (8.74 +/- 0.67 versus 6.76 +/- 0.54 mmHg). The extended Bernoulli equation performed better (6.57 +/- 0.53 mmHg), although errors remained at low TPG(net). TPG(net) estimations using the PPE were always close to zero. Total TKE and viscous dissipation correlated strongly with TPG(net) for each geometry (r(2) > 0.93) and moderately considering all geometries (r(2) = 0.756 and r(2) = 0.776, respectively). TKE estimates were accurate and minorly impacted by resolution. Viscous dissipation was overall underestimated and resolution dependent. Conclusion: Several parameters overestimate or are not linearly related to TPG(net) and/or depend on spatial resolution. Considering idealized axisymmetric geometries and in absence of noise, TPG(net) was best estimated using the extended Bernoulli equation. (C) 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance.

Place, publisher, year, edition, pages
WILEY-BLACKWELL , 2016. Vol. 75, no 4, p. 1808-1821
Keywords [en]
pressure loss; phase contrast magnetic resonance imaging; aortic valve disease; aortic coarctation
National Category
Clinical Medicine
Identifiers
URN: urn:nbn:se:liu:diva-127426DOI: 10.1002/mrm.25772ISI: 000372910900043PubMedID: 26016805OAI: oai:DiVA.org:liu-127426DiVA, id: diva2:925262
Note

Funding Agencies|European Research Council [310612]; Swedish Research Council

Available from: 2016-05-01 Created: 2016-04-26 Last updated: 2018-10-10
In thesis
1. Towards Personalized Models of the Cardiovascular System Using 4D Flow MRI
Open this publication in new window or tab >>Towards Personalized Models of the Cardiovascular System Using 4D Flow MRI
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Current diagnostic tools for assessing cardiovascular disease mostly focus on measuring a given biomarker at a specific spatial location where an abnormality is suspected. However, as a result of the dynamic and complex nature of the cardiovascular system, the analysis of isolated biomarkers is generally not sufficient to characterize the pathological mechanisms behind a disease. Model-based approaches that integrate the mechanisms through which different components interact, and present possibilities for system-level analyses, give us a better picture of a patient’s overall health status.

One of the main goals of cardiovascular modelling is the development of personalized models based on clinical measurements. Recent years have seen remarkable advances in medical imaging and the use of personalized models is slowly becoming a reality. Modern imaging techniques can provide an unprecedented amount of anatomical and functional information about the heart and vessels. In this context, three-dimensional, three-directional, cine phase-contrast (PC) magnetic resonance imaging (MRI), commonly referred to as 4D Flow MRI, arises as a powerful tool for creating personalized models. 4D Flow MRI enables the measurement of time-resolved velocity information with volumetric coverage. Besides providing a rich dataset within a single acquisition, the technique permits retrospective analysis of the data at any location within the acquired volume.

This thesis focuses on improving subject-specific assessment of cardiovascular function through model-based analysis of 4D Flow MRI data. By using computational models, we aimed to provide mechanistic explanations of the underlying physiological processes, derive novel or improved hemodynamic markers, and estimate quantities that typically require invasive measurements. Paper I presents an evaluation of current markers of stenosis severity using advanced models to simulate flow through a stenosis. Paper II presents a framework to personalize a reduced-order, mechanistic model of the cardiovascular system using exclusively non-invasive measurements, including 4D Flow MRI data. The modelling approach can unravel a number of clinically relevant parameters from the input data, including those representing the contraction and relaxation patterns of the left ventricle, and provide estimations of the pressure-volume loop. In Paper III, this framework is applied to study cardiovascular function at rest and during stress conditions, and the capability of the model to infer load-independent measures of heart function based on the imaging data is demonstrated. Paper IV focuses on evaluating the reliability of the model parameters as a step towards translation of the model to the clinic.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2018. p. 71
Series
Linköping University Medical Dissertations, ISSN 0345-0082 ; 1641
Keywords
Magnetic resonance imaging, 4D Flow MRI, Computational modelling, Blood flow, Hemodynamics
National Category
Biomedical Laboratory Science/Technology
Identifiers
urn:nbn:se:liu:diva-151751 (URN)10.3384/diss.diva-151751 (DOI)9789176852170 (ISBN)
Public defence
2018-11-05, Belladonna, Hus 511, Ingång 76, Våning 9, Campus US, Linköping, 13:00 (English)
Opponent
Supervisors
Funder
EU, European Research Council, 310612Swedish Research Council, 621-2014-6191The Swedish Heart and Lung Association, 20140398
Available from: 2018-10-05 Created: 2018-10-04 Last updated: 2019-09-30Bibliographically approved

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Casas Garcia, BelénLantz, JonasDyverfeldt, PetterEbbers, Tino

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