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Development of a deep learning approach to reconstruct multiparametric magnetic resonance correlation spectra

  • Magnetic resonance imaging (MRI) has assumed a central role in modern medical imaging thanks to its ability to provide detailed images of the inside of the body. This thesis aims to push the boundaries of conventional MRI by demonstrating two deep learning approaches to reconstruct multparametic correlation spectra. The multi-model approach determines each peak in the spectrum with a separate model, but does not generalise and requires knowledge of the number of spectra being searched for. The residual approach is an iterative process. The strongest signal is determined from the current residual and subtracted from the residual. The spectrum is created using the information obtained. This process is repeated until the residual no longer contains any information. A proof of concept was carried out with the restriction that the number of iterations was determined in advance. This thesis shows that the residual approach works and proves to be promising for future work.

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Metadaten
Author:Tobias Sebastian Lachermeier
Language:English
Document Type:Bachelor Thesis
Reviewer(s) at THI:Marion Irene Menzel, Matthias Eckert
Publishing Institution:Technische Hochschule Ingolstadt
Date of first Publication:2024/01/29
Year of first Publication:2024
Place of publication:Ingolstadt
Pages:40
URN:urn:nbn:de:bvb:573-45223
Faculties / Institutes / Organizations:Fakultät Informatik
Degree Programmes:Künstliche Intelligenz (B. Sc.)
Licence (German):License Logo Urheberrechtsschutz
Release Date:2024/02/26