Title: Signal Processing and Classification for Magnetic Resonance Spectroscopy with Clinical Applications (Signaalverwerking en classificatie van magnetische resonantie spectroscopie met klinische toepassingen)
Other Titles: Signal Processing and Classification for Magnetic Resonance Spectroscopy with Clinical Applications
Authors: Croitor Sava, Anca Ramona
Issue Date: 29-Nov-2011
Abstract: Over the past decades, Magnetic Resonance Imaging (MRI) has taken a leading role in the study of the human body and it is widely used in clinical diagnosis. In vivo and ex vivo Magnetic Resonance Spectroscopic (MRS) techniques can additionally provide valuable metabolic information as compared to MRI and are gaining more clinical interest. The analysis of MRS data is a complex procedure and requires several preprocessing steps aiming to improve the quality of the data and to extract the most relevant features before any classification algorithm can be successfully applied. In this thesis, a new approach to quantify magnetic resonance spectroscopic imaging (MRSI) data and therefore to obtain improved metabolite estimates is proposed. Then, an important part is focusing on improving the diagnosis of glial brain tumors which are characterized by an extensive heterogeneity since various intratumoral histopathological properties such as viable tumor cells, necrotic tissue and infiltration of tumor cells into normal tissue can be identified in the tumor region. For a reliable diagnosis of the glial tumor type and grade this thesis proposes a first screening between these intratumoral histopathological properties. To this aim, cluster analysis and several blind source separation methods are tested on ex vivo HR-MAS and in vivo MRSI data. Moreover, several approaches to fuse multimodal information coming from MRI, MRSI and HR-MAS spectroscopy for the classification of glial brain tumors are considered. MRS techniques are nowadays successfully considered for the analysis of body fluids. A pilot research to study the amniotic fluid from fetuses with congenital diaphragmatic hernia using high resolution MRS is proposed.
Publication status: published
KU Leuven publication type: TH
Appears in Collections:ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
Leuven Statistics Research Centre (LStat)

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