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Bridging the gap between measurements and modelling: a cardiovascular functional avatar
Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för kardiovaskulär medicin. Linköpings universitet, Medicinska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för kardiovaskulär medicin. Linköpings universitet, Medicinska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.ORCID-id: 0000-0003-1942-7699
Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för kardiovaskulär medicin. Linköpings universitet, Medicinska fakulteten.
Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
Vise andre og tillknytning
2017 (engelsk)Inngår i: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, artikkel-id 6214Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Lumped parameter models of the cardiovascular system have the potential to assist researchers and clinicians to better understand cardiovascular function. The value of such models increases when they are subject specific. However, most approaches to personalize lumped parameter models have thus far required invasive measurements or fall short of being subject specific due to a lack of the necessary clinical data. Here, we propose an approach to personalize parameters in a model of the heart and the systemic circulation using exclusively non-invasive measurements. The personalized model is created using flow data from four-dimensional magnetic resonance imaging and cuff pressure measurements in the brachial artery. We term this personalized model the cardiovascular avatar. In our proof-of-concept study, we evaluated the capability of the avatar to reproduce pressures and flows in a group of eight healthy subjects. Both quantitatively and qualitatively, the model-based results agreed well with the pressure and flow measurements obtained in vivo for each subject. This non-invasive and personalized approach can synthesize medical data into clinically relevant indicators of cardiovascular function, and estimate hemodynamic variables that cannot be assessed directly from clinical measurements.

sted, utgiver, år, opplag, sider
Nature Publishing Group, 2017. Vol. 7, artikkel-id 6214
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-140069DOI: 10.1038/s41598-017-06339-0ISI: 000406260100018PubMedID: 28740184Scopus ID: 2-s2.0-85025821468OAI: oai:DiVA.org:liu-140069DiVA, id: diva2:1136565
Merknad

Funding Agencies|European Research Council [310612]; Swedish Research Council [2014-6191]

Tilgjengelig fra: 2017-08-28 Laget: 2017-08-28 Sist oppdatert: 2018-10-10bibliografisk kontrollert
Inngår i avhandling
1. Towards Personalized Models of the Cardiovascular System Using 4D Flow MRI
Åpne denne publikasjonen i ny fane eller vindu >>Towards Personalized Models of the Cardiovascular System Using 4D Flow MRI
2018 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Linköping: Linköping University Electronic Press, 2018. s. 71
Serie
Linköping University Medical Dissertations, ISSN 0345-0082 ; 1641
Emneord
Magnetic resonance imaging, 4D Flow MRI, Computational modelling, Blood flow, Hemodynamics
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-151751 (URN)10.3384/diss.diva-151751 (DOI)9789176852170 (ISBN)
Disputas
2018-11-05, Belladonna, Hus 511, Ingång 76, Våning 9, Campus US, Linköping, 13:00 (engelsk)
Opponent
Veileder
Forskningsfinansiär
EU, European Research Council, 310612Swedish Research Council, 621-2014-6191The Swedish Heart and Lung Association, 20140398
Tilgjengelig fra: 2018-10-05 Laget: 2018-10-04 Sist oppdatert: 2019-02-15bibliografisk kontrollert

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