Phase Contrast MRI Measurements

Title: Simulation of Phase Contrast MRI Measurements from Numerical Flow Data

Phase-contrast magnetic resonance imaging (PC-MRI) is a powerful tool for measuring blood flow and has a wide range of cardiovascular applications. Simulation of PC-MRI from numerical flow data would be useful for addressing the data quality of PC-MRI measurements and to study and understand different artifacts. It would also make it possible to optimize imaging parameters prior to the PC-MRI measurements and to evaluate different methods for measuring wall shear stress.Based on previous studies a PC-MRI simulation tool was developed. An Eulerian-Lagrangian approach was used to solve the problem. Computational fluid dynamics (CFD) data calculated on a fix structured mesh (Eulerian point of view) were used as input. From the CFD data spin particle trajectories were computed. The magnetization of the spin particle is then evaluated as the particle travels along its trajectory (Lagrangian point of view).The simulated PC-MRI data were evaluated by comparison with PC-MRI measurements on an in vitro phantom. Results indicate that the PC-MRI simulation tool functions well. However, further development is required to include some of the artifacts. Decreasing the computation time will make more accurate and powerful simulations possible. Several suggestions for improvements are presented in this report.


1 Introduction
1.1 Formulation of the Problem
1.2 Aim of the Thesis
2 Background
2.1 Magnetic Resonance Imaging
2.1.1 Spin Physics
2.1.2 Spatial Encoding of the MR Signal
2.2 Phase Contrast Magnetic Resonance Imaging
2.2.1 PC-MRI Artifacts
3 Methods and Material
3.1 Numerical Simulations of PC-MRI
3.1.1 Tracking the Particles
3.1.2 Pulse Sequence
3.1.3 Solving the Bloch Equations
3.1.4 Creating the Image
viiviii Contents
3.1.5 Accuracy
3.2 Validation
3.2.1 Flow Phantom
3.2.2 PC-MRI Measurements
3.2.3 Computational Fluid Dynamics Calculations
3.2.4 PC-MRI Simulation Parameters
4 Results
4.1 Simulated Data
5 Discussion
5.1 Interpretation of the Results
5.2 Future Work
5.2.1 Computation Time
5.3 Possible Fields of Application
5.4 Conclusion

Author: Petersson, Sven

Source: Linköping University

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