Signal Processing and Classification for Magnetic Resonance Spectroscopy with Clinical Applications (Signaalverwerking en classificatie van magnetische resonantie spectroscopie met klinische toepassingen)

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.status: publishe

Identifying constituent tumor tissues subclasses in HR-MAS spectra using advanced blind source separation techniques

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Metabolite quantification of MRSI data with an automatic correction for the chemical shift displacement artifact

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Metabolite variability in gliomas using SENSE-accelerated Magnetic Resonance Spectroscopic Imaging at 3T: a statistical perspective

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Hierarchical non-negative matrix factorization applied to in vivo 3T MRSI prostate data for automatic detection and visualization of tumours

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NMF in MR spectroscopy

© Springer-Verlag Berlin Heidelberg 2016. All rights reserved. Nowadays, magnetic resonance spectroscopy (MRS) represents a powerful nuclear magnetic resonance (NMR) technique in oncology since it provides information on the biochemical profile of tissues, thereby allowing clinicians and radiologists to identify in a non-invasive way the different tissue types characterising the sample under investigation. The main purpose of the present chapter is to provide a review of the most recent and significant applications of non-negative matrix factorisation (NMF) to MRS data in the field of tissue typing methods for tumour diagnosis. Specifically, NMF-based methods for the recovery of constituent spectra in ex vivo and in vivo brain MRS data, for brain tissue pattern differentiation using magnetic resonance spectroscopic imaging (MRSI) data and for automatic detection and visualisation of prostate tumours, will be described. Furthermore, since severalNMFimplementations are available in the literature, a comparison in terms of pattern detection accuracy of some NMF algorithms will be reported and discussed, and the NMF performance forMRS data analysis will be compared with that of other blind source separation (BSS) techniques.status: publishe

Unsupervised brain tumor tissue differentiation based on MRSI with correction for the chemical shift displacement artifact

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Hierarchical non-negative matrix factorization (hNMF) : a tissue pattern differentation method for glioblastoma multiforme diagnosis using MRSI

MRSI has shown potential in the diagnosis and prognosis of glioblastoma multiforme (GBM) brain tumors, but its use is limited by difficult data interpretation. When the analyzed MRSI data present more than two tissue patterns, conventional non-negative matrix factorization (NMF) implementation may lead to a non-robust estimation. The aim of this article is to introduce an effective approach for the differentiation of GBM tissue patterns using MRSI data. A hierarchical non-negative matrix factorization (hNMF) method that can blindly separate the most important spectral sources in short-TE ¹H MRSI data is proposed. This algorithm consists of several levels of NMF, where only two tissue patterns are computed at each level. The method is demonstrated on both simulated and in vivo short-TE ¹H MRSI data in patients with GBM. For the in vivo study, the accuracy of the recovered spectral sources was validated using expert knowledge. Results show that hNMF is able to accurately estimate the three tissue patterns present in the tumoral and peritumoral area of a GBM, i.e. normal, tumor and necrosis, thus providing additional useful information that can help in the diagnosis of GBM. Moreover, the hNMF results can be displayed as easily interpretable maps showing the contribution of each tissue pattern to each voxel.status: publishe

High-resolution 1H NMR Spectroscopy Discriminates Amniotic Fluid of Fetuses with Congenital Diaphragmatic Hernia from Healthy Controls

Lung hypoplasia in congenital diaphragmatic hernia (CDH) is a life-threatening birth defect. Severe cases can be offered tracheal occlusion to boost prenatal lung development, although defining those to benefit remains challenging. Metabonomics of (1)H NMR spectra collected from amniotic fluid (AF) can identify general changes in diseased versus healthy fetuses. AF embodies lung secretions and hence might contain pulmonary next to general markers of disease in CDH fetuses. AF from 81 healthy and 22 CDH fetuses was collected. NMR spectroscopy was performed at 400 MHz to compare AF from fetuses with CDH against controls. Several advanced feature extraction methods based on statistical tests that explore spectral variability, similarity, and dissimilarity were applied and compared. This resulted in the identification of 30 spectral regions, which accounted for 80% variability between CDH and controls. Combination with automated classification discriminates AF from CDH versus healthy fetuses with up to 92% accuracy. Within the identified spectral regions, isoleucine, leucine, valine, pyruvate, GABA, glutamate, glutamine, citrate, creatine, creatinine, taurine, and glucose were the most concentrated metabolites. As the metabolite pattern of AF changes with fetal development, we have excluded metabolites with a high age-related variability and repeated the analysis with 12 spectral regions, which has resulted in similar classification accuracy. From this analysis, it was possible to distinguish between AF from CDH fetuses versus healthy controls independent of gestational age.status: publishe