Signal processing and classification for magnetic resonance spectroscopic data with clinical applications (2011)
Abstract / truncated to 115 words
Over the last decades, Magnetic Resonance Imaging (MRI) has taken a leading role in the study of 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 ... toggle 17 keywordsbioi – DSP – control – numalg – canonical correlations – factor analysis – RMSE – SVD – NMR – signal separation – signal-to-noise ratio – spectral estimation – biotechnology – clinical bio informatics – clustering – metabolomica – preprocessing
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