Print Email Facebook Twitter Super Resolution Techniques Applied to Low-Field MRI Title Super Resolution Techniques Applied to Low-Field MRI Author Ippolito, Giulia (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Numerical Analysis) Contributor van Gijzen, M.B. (mentor) de Leeuw den Bouter, M.L. (mentor) van Gennip, Y. (graduation committee) Webb, A. (graduation committee) Degree granting institution Delft University of Technology Programme Applied Mathematics Date 2020-10-19 Abstract This work is part of the low-fieldMRI project, which aims to bring portable, affordable, low-fieldMRI scanners to low-income countries. Replacing the superconducting magnets of conventional scanners with standard ones can significantly reduce the costs, but it also has a negative impact on the Signal-to-Noise Ratio (SNR). In order to circumvent this problem, Super Resolution (SR) techniques may be used. In this thesis, standard and Deep Learning (DL) SR techniques are presented. For standard SR, two types of regularization are considered: Tikhonov and Total Variation (TV). Then, the problemis solved using CGLS and ADMM algorithms respectively. From our analysis, we could conclude that TV outperforms Tikhonov regularization, yielding promising results. Then, two 2D DL models, SRCNN and ReCNN, were implemented and trained on two commonly used SR datasets: T91 and Kirby21. Both networks managed to reconstruct the LR scans surprisingly well, with ReCNN yielding the best results when trained on both datasets. DL methods evidently outperform standard SR and can achieve a visual quality comparable to the one of a scan directly acquired in higher resolution. A 3D extension of these networks was also considered, but, although it led to an improvement, it did not perform as well as the 2D models. We attribute this to the lack of time, which did not allow us to extensively explore this possibility. Subject Super resolutionLow-Field Magnetic Resonance ImagingDeep learningConjugate Gradient To reference this document use: http://resolver.tudelft.nl/uuid:f1603e81-d5b6-46fb-8c39-83842c518d31 Part of collection Student theses Document type master thesis Rights © 2020 Giulia Ippolito Files PDF MasterThesis_GiuliaIppolito.pdf 30.35 MB Close viewer /islandora/object/uuid:f1603e81-d5b6-46fb-8c39-83842c518d31/datastream/OBJ/view