Image Reconstruction for a Handheld Low-Field MRI Scanner via Deep Learning

Master thesis (2022)

Authors

A.E. Bekkering Mechanical Engineering

Contributors

M.B. van Gijzen Numerical Analysis - EEMCS (mentor)
R.F. Remis Signal Processing Systems - EEMCS (mentor)
F.M. Vos ImPhys/Computational Imaging - AS (mentor)
Faculty
Mechanical Engineering, Mechanical Engineering
Copyright: © 2022 Aleid Bekkering
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Published Date

10-05-2022

Language

English

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Faculty

Mechanical Engineering

Abstract

MRI has been used worldwide as an imaging modality for decades due to its ability to distinguish between soft tissues. However, MRIs cannot be used in all situations due to their extensive hard- and software. For this reason, research in the field of low-field MRI has been conducted. Low-field MRI systems do not use superconducting magnets but restive, or in this research, permanent magnets. This reduces the costs of these scanners, but these same changes result in a poorer signal-to-noise ratio (SNR) than high-field scanners. Based on this hardware, research in single-sided scanners has emerged, and various research groups have been able to reconstruct images with these scanners. They are based on conventional MRI hardware including a radiofrequency (RF) coil, permanent magnets and gradients coils for encoding. In collaboration with the Technical University (TU) Delft, a handheld scanner has been constructed in the Leiden University Medical Center (LUMC). This scanner consists of an RF coil and a permanent magnet and does not have a gradient coil. The inhomogeneity of the 50 can be used for spatially encoding the signal based on translations. The SNR in images is improved with model-based image reconstruction. Although, in theory, this set-up should work, the image results so far have been somewhat disappointing. Therefore, in this thesis, we have investigated a different approach, using a neural network. In this research, the correlation between the received signal of a measurement and a simulation of the same shape is found to be greater than the correlation between other simulations and the received signal. Due to this proven correlation, an image reconstruction deep learning algorithm is constructed based on the AUTOMAP model. Simulated signals are reconstructed into images with promising results; however, the algorithm cannot reconstruct mea- sured data. Therefore, suggestions for future improvement include improving the simulated data set and adding more real measurements that would allow for training on a measured data set.