Numerical Simulation of Sampling Capabilities of Spiral Trajectory and Echo-Planer Imaging in Magnetic Resonance Fingerprinting
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Date
2023-01
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Addis Ababa University
Abstract
Magnetic Resonance Fingerprinting (MRF) is a novel method proposed to solve the limitations of quantitative Magnetic Resonance Imaging (qMRI). One of the ways MRF accelerates data acquisition is by using various sampling mechanisms to undersample the k-space. In this thesis, the effectiveness of spiral sampling and accelerated cartesian sampling using multi-shot Echo-Planar Imaging (EPI) are compared by keeping the other steps in the MRF framework constant. Dictionary atoms were generated by using Bloch simulation. During the data acquisition, a realistic simulation framework based on the Bloch equation is built and implemented in a MATLAB platform. An inversion-recovery balanced steady-state free precession sequence was used in simulating the series images as well as the dictionary. The dot product of the simulated signal evolution and the dictionary of predicted signal evolutions is used for pattern matching. To check the efficacy of the methodology, cylindrical and brain numerical phantoms were used. The respective percentage errors in T1, T2 and off-resonance quantification were 2.6%, 2.3%, and 14% for spiral-MRF and 39%, 43%, and 124% for EPI-MRF. The results showed that spiral undersampling produces superior results in a close match with the ground truth compared to multi-shot EPI, showing the great promises of spiral trajectory to be used as an effective sampling tool in MRF. The MRF simulator developed in this thesis work effectively simulates the image acquisition process of an MRI machine and has consistently produced accurate results. The obtained results are generally comparable to those reported in other studies that utilized real scanners
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Keywords
Spiral Trajectory, Echo-Planer Imaging, Magnetic Resonance Fingerprinting