Toward A Simulation Framework For Magnetic Resonance Fingerprinting Using Spiral Underdamping
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Date
2020-12
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Addis Ababa University
Abstract
Magnetic Resonance (MR) is a powerful and multipurpose measurement technique to look inside the human body using magnetic properties of the body non-invasively. Quantification of tissue properties including the relaxation parameters has long been a goal of magnetic resonance imaging (MRI), to provide a basis for diagnosis, longitudinal study and, inter-patient comparability. However, prolonged acquisition times have hindered the usage of quantification for clinical applications. Magnetic Resonance Fingerprinting (MRF) was introduced as a promising technique for simultaneous and fast quantification of multiple tissue parameters through a new approach to data acquisition and postprocessing. However, the development and optimization of MRF process is time-consuming, expensive which requisite repetitive experiment and often needs a synthetic phantom or human subject to test it on the real scanner and detect the results. This work aimed to develop an implementation of a Simulation Framework for MRF based on spiral under-sampling. Simulation framework for magnetic resonance fingerprinting (MRF) along with evaluating the effect of noise on the parameter map is significantly studied by using custom generated phantom and phantom generated from the brain web of a simulated database. The undersampling capability of MRF is significantly studied and evaluated by comparing the parameter map that is generated at each undersampling level. Significant undersampling is used to make the MRF time-efficient with several undersampling folds and undersampling with the factor of 24-fold is substantially evaluated and results in acceptable tissue quantification result irrespective of the undersampling artifacts. A comparison between the proposed MRF simulation and the existing simulation used for MRF was done and the proposed methods showed superior results to simulate the effect of gradient on the reconstructed image and at the quantified parameter map. The effect of noise on the parameter map is evaluated by adding different levels of noise on the simulated kspace signal. At a given undersampling level, as the level of noise increases, its effect on the parameter maps gets more and more pronounced specifically in the off-resonance map.
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Simulation Framework, Magnetic Resonance, Fingerprinting, Spiral Undersampling