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Deep Learning for the prediction of RASER-MRI profiles
Linköping University, Department of Science and Technology, Media and Information Technology.
Linköping University, Department of Science and Technology, Media and Information Technology.
2023 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Magnetic resonance imaging (MRI) is a critical diagnostic tool in medical practice, enabling non-invasive visualization of anatomy and physiological processes. Nonetheless, MRI has inherent spatial resolution limitations, which may limit its diagnostic capabilities. Recently, a new technology employing Radio-frequency Amplification by Stimulated emission of Radiation (RASER) has emerged to improve MRI resolution. Similar to a laser, RASER-MRI signals spontaneously emerge without the need for a radio frequency pulse(RF), which additionally enhances the safety of the process. However, RASER-MRI images frequently exhibit a significant presence of image artifacts due to the nonlinear behavior between image slices. This master’s thesis aims to determine whether image artifacts can be eliminated using deep artificial neural networks. The neural networks were trained on purely synthetic data, due to the complexity of real RASER experiments. The implementation was split into three phases. The first phase focused on the reconstruction of 1D RASER profiles. The test done during this phase showed that the reconstruction was preferably made with a Convolutional Neural Network (CNN). The CNN does not require knowledge of the total population inversion, and the ideal input was the most volatile RASER spectrum. The second phase was dedicated to reconstructing simulated RASER-MRI images. This phase started with the creation of a random RASER-MRI image generator which was used to generate the training and testing data. The reconstruction was successful and was further enhanced with an image-to-image Unet. The entire deep learning pipeline did not suffice for real data, which sparked the third phase. The third phase focused on simulating more realistic RASER data. The new data improved the result, however, the reconstruction did not suffice. Further research needs to be done into ways to make the simulation more realistic to improve the reconstruction of the real RASER-MRI image. However, this project concludes that simulated RASER-spectra can be reconstructed using deep learning.

Place, publisher, year, edition, pages
2023. , p. 81
Keywords [en]
MRI, NMR, AI, Deep Learning, RASER-MRI, Media Technology, KIT, LIU, CNN, Autoencoder
Keywords [sv]
Djupinlärning, Medieteknik, Magnetröntgen
National Category
Media Engineering
Identifiers
URN: urn:nbn:se:liu:diva-199640OAI: oai:DiVA.org:liu-199640DiVA, id: diva2:1819731
External cooperation
Institute of Microstructure Technology, Karlsruhe Institute of Technology
Subject / course
Media Technology
Presentation
2023-10-02, TP52, Campus Norrköping, Bredgatan 33, 602 21 Norrköping, Sverige, Norrköping, 22:41 (English)
Supervisors
Examiners
Available from: 2023-12-15 Created: 2023-12-14 Last updated: 2023-12-15Bibliographically approved

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CiteExportLink to record
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