Turbulence is described by its main characteristics that are irregularity in time and space In hydromechanics, turbulence is referred to as chaotic movement superimposed on the flow of a fluid In the human body, turbulence typically arises in flows through heart valve prostheses and vascular or valvular stenoses.
This thesis exploits a magnetic resonance imaging (MRI) phenomenon referred to as signal loss in order to develop a method for estimating turbulence intensity in blood flow. MRI measurements were carried out on an appropriate flow phantom. The turbulence intensity results obtained by means of the proposed method were compared with previously known turbulence intensity results. The comparison indicates that the proposed method has great potential for estimation of turbulence intensity.
Contents]
1 Introduction
1.1 Formulation of the problem
1.2 Aim of the thesis
2 Background
2.1 The cardiovascular system
2.1.1 Arterial stenoses
2.1.2 Heart valve prosthesis
2.2 Turbulence
2.2.1 Reynolds number
2.2.2 Turbulent flow in the human body
2.3 Magnetic resonance imaging
2.3.1 Physics
2.3.2 Imaging principles
2.3.3 Phase contrast magnetic resonance imaging
3 Methods and material
3.1 Statistical tools
3.2 From turbulent flow to estimated turbulence intensity
3.2.1 The MR-signal
3.2.2 The impact of turbulent flow on the MR-signal
3.2.3 An estimate of standard deviation
3.2.4 An estimate of turbulence intensity
3.3 Validation
3.3.1 Flow phantom
3.3.2 Flow system
3.3.3 MRI measurements
3.3.4 Processing data
4 Results
4.1 Magnitude
4.2 Standard deviation
4.2.1 Maps of standard deviation
4.2.2 Centerline standard deviation
4.3 Comparison of turbulence intensity results
5 Discussion .
5.1 Interpretation of the results
5.1.1 Lateral displacement
5.1.2 Velocity encoding range
5.1.3 Effects of noise and other artefacts
5.1.4 Measurement arrangement
5.2 Possible fields of application
5.2.1 Turbulence as a diagnostic tool
5.2.2 Uncertainty of velocity measurements
5.2.3 Correct the impact of turbulence on pressure calculations
5.2.4 Computational fluid dynamics
5.2.5 Non-medical use
5.3 Future work impact of the velocity encoding range on the estimate of standard deviation
5.3.2 Study the impact of artefacts on the estimate
5.3.3 In-vivo measurements
6 References
Author: Dyverfeldt, Petter
Source: Linköping University
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