Advanced Signal Processing for Magnetic Resonance Spectroscopy (Geavanceerde signaalverwerking voor magnetische resonantie spectroscopie)

Assertive diagnosis of cancer, Alzheimer s disease, epilepsy and other metabolic diseases is essentialto provide patients with the adequate treatment. Recently, different invasive and non-invasive techniques have been developed for this purpose, nevertheless, due to their harmless properties the non-invasive techniques have gained more value. Magnetic Resonance is a well-known non-invasive technique that provides spectra (metabolite peaks) and images (anatomical structures) of the examined tissue. In Magnetic Resonance Spectroscopy (MRS), molecules containing certain excitable nuclei, such as 1H, provide the metabolite information. As a consequence, the peaks in the MR spectra correspond to observable metabolites which are the biomarkers of diseases. Finally, metabolite concentrations arecomputed and compared against normal values in order to establish the diagnosis. The method to obtain such amplitudes is also called quantification and its accuracy is essential for diagnosis assessment. Quantification of MRS signals is affected by a relatively low signal-to-noise ratio (SNR), a residual water resonance, lineshape distortions, overlapping resonances and underlying macromolecules and lipids affecting their baseline. Our ultimate goal was, therefore, the development of signal processing toolsand algorithms to improve the quantification of MRS signals. For this, we first proposed a heuristic method to improve the residual water filtering using Hankel Singular Value Decomposition (HSVD). Additionally, a method for lineshape estimation of distorted MR spectra was developed and evaluated in simulated, in vitro and in vivo signals. Furthermore, a baseline approach was implemented via a parametric modelling method based on prior knowledge acquired from a set of in vivo macromolecular signals (measured via inversion recovery), which aims at avoiding long measurements and allowing a flexible set of baseline components. Finally, for the analysis of quantification results we focused on an automatic evaluation of the residual, thereby benefiting MRS spectral analysis in the clinic. This work wasdeveloped within the context of the European project FAST (MRTN-CT-2006-035801) - Advanced Signal Processing for Ultra Fast Magnetic Resonance -, which was a Research and Training Network granted by Marie Curie Actions in the 6th Framework Program (2007-2010), http://fast-mrs.eu.1. Introduction 2. Magnetic Resonance Spectroscopy (MRS) signals 3. Quantification 4. Filtering of residual water 5. Lineshape estimation in MRS signals 6. Parametric method for baseline estimation of MRS signals 7. Residual analysis 8. Summary and further researchnrpages: 145status: publishe