Title
Compressed Sensing in Low-Field MRI: Using Multiplicative Regularization
Author
Verseput, Marien (TU Delft Electrical Engineering, Mathematics and Computer Science)
Contributor
Remis, R.F. (mentor)
de Vos, B. (graduation committee)
Krasnov, O.A. (graduation committee)
Degree granting institution
Delft University of Technology
Programme
Electrical Engineering
Date
2023-12-01
Abstract
Magnetic resonance imaging (MRI) is a non-invasive tool to image the body’s anatomy and physiology, but suffers from long scan times. Compressed Sensing (CS) is used to accelerate MRI scans by incoherently taking fewer measurements and using a nonlinear optimization algorithm to image the undersampled data. Convex optimization techniques are generally used for image reconstruction. Minimizing a data fidelity term along with two regularization terms, which are a total variation (TV) based and a wavelet transform based function, is a standard procedure in CS-MRI. Regularization parameters are needed to balance the different terms, but it is impossible to know upfront what the optimal regularization parameters are to get the desired output. A consequence is that the algorithm of choice needs to be executed many times for many different values of the regularization parameters, which is a time-consuming process requiring knowledge of the algorithm.
In this work we rewrite and implement the regularization functions in a multiplicative manner by multiplying the data fidelity term with the regularization terms, thereby eliminating the need to tune the regularization parameters. Moreover, we include a region of support (ROS) mask to further accelerate reconstruction. The performance of different combinations of regularization functions and reconstruction algorithms are validated on a simulation study and various experiments on a low-field MRI scanner. This also shows the capability of CS applied to low-field MRI, which has lower signal-to-noise ratio compared to conventional MRI. Of all proposed methods, a nonlinear conjugate gradient method applied to the fully multiplicatively regularized objective function shows the most robust performance.
Subject
MRI
Low-field MRI
Compressed sensing
Multiplicative regularization
Image reconstruction
To reference this document use:
http://resolver.tudelft.nl/uuid:e785aa3d-923e-4999-a70a-3b986b01a088
Embargo date
2025-12-01
Part of collection
Student theses
Document type
master thesis
Rights
© 2023 Marien Verseput