"Silver" mode for the heavy Higgs search in the presence of a fourth SM family

We investigate the possible enhancement to the discovery of the heavy Higgs boson through the possible fourth SM family heavy neutrino. Using the channel h-> v4 v4->mu W mu W, it is found that for certain ranges of Higgs boson and v4 masses LHC could discover both of them simultaneously with 1 fb^-1 integrated luminosity

Unsupervised and Semi-supervised Non-negative Matrix Factorization Methods for Brain Tumor Segmentation using Multi-parametric MRI Data

Gliomas represent about 80% of all malignant primary brain tumors. Despite recent advancements in glioma research, patient outcome remains poor. Magnetic resonance imaging (MRI) has become the imaging modality of choice in the management of brain tumor patients. Over the past decade, advanced MRI modalities, such as perfusion-weighted imaging, diffusion-weighted imaging and magnetic resonance spectroscopic imaging have gained interest in the clinical field, and their added value has been recognized. Tumor segmentation plays an important role in treatment planning as well as during follow-up. Manual segmentation by a clinical expert is currently the gold standard, but it is a tedious and time-consuming task. Clinical practice would benefit from accurate and automated volumetric delineation of the tumor and its sub-compartments. Throughout this PhD, methods have been developed for automated segmentation and characterization of brain tumors. The proposed methods are based on an unsupervised learning technique called non-negative matrix factorization (NMF). NMF provides an additive parts-based representation of the input data, revealing the basic components which are present. Applied to the multi-parametric MR imaging data of a brain tumor patient, NMF is able to extract tissue-specific signatures as well as the relative proportions of the different tissue types in each voxel. Being an unsupervised method, NMF cannot benefit from an extensive training dataset to learn decision boundaries between tissue classes, but it is directly applicable to any multi-parametric MRI dataset of any individual patient.Abstract Contents List of Figures List of Tables 1. Introduction 2. Non-negative matrix factorization and validation metrics 3. Multi-parametric MRI datasets and pre-processing 4. Hierarchical non-negative matrix factorization to characterize brain tumor heterogeneity using MP-MRI data 5.The successive projection algorithm as an initialization method for brain tumor segmentation using NMF 6. Comparison of unsupervised classification methods for brain tumor segmentation using MP-MRI data 7. Semi-automated brain tumor segmentation on MP-MRI data using regularized NMF 8. Application of semi-automated regularized NMF to the BRATS 2013 Leaderboard dataset 9. Conclusions and future perspectives Appendix A: Validation scores individual UZ Leuven patients Bibliography Curriculum vitae List of publicationsnrpages: 244status: publishe

Biopsy targeting based on perfusion and diffusion MR related parameters

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Comparison of chemometrics strategies for the spectroscopic monitoring of active pharmaceutical ingredients in chemical reactions

The Process Analytical Technology (PAT) initiative promoted by the Food and Drug Administration (FDA) encourages pharmaceutical companies to increase the use of new analytical technologies to perform constant monitoring of the critical quality attributes (CQA), allowing a better understanding and a better control of the process. This paper presents a practical framework based on different dimension-reduction methods as well as calibration methods aimed at following over time chemical experiments organized in batches. To illustrate it, this paper uses pharmaceutical data collected in a research and development context towards industrial production. This methodological framework aims to reach two objectives. The first objective is to visualize and interpret in real time, or off-line, the kinetics of chemical reactions using the following dimension-reduction methods: principal component analysis (PCA), non-negative matrix factorization (NMF) and multivariate curve resolution (MCR). The results show that, due to their additional constraints, NMF and MCR allow a better interpretability of chemical reactions than PCA with a comparable quality of fit. Moreover, eventough NMF and MCR come from different fields, their algorithms share many similarities and produce close results. The second objective is to predict chemical component concentrations over time. For this second objective, the partial least squares regression (PLSR) is used in a one-step approach and compared with a two-step approach combining multivariate regression with PCA, NMF or MCR. The results show that spectra or scores obtained from unsupervised approaches PCA, NMF or MCR can be used to predict concentrations of the main chemical compounds continuously over all the time of the reaction with a good precision and with a gain of interpretability. For both objectives, possible model validation indices are also discussed including a leave-one-batch-out approach

Comparison of chemometrics strategies for the spectroscopic monitoring of active pharmaceutical ingredients in chemical reactions

The Process Analytical Technology (PAT) initiative promoted by the Food and Drug Administration (FDA) encourages pharmaceutical companies to increase the use of new analytical technologies to perform constant monitoring of the critical quality attributes (CQA), allowing a better understanding and a better control of the process. This paper presents a practical framework based on different dimension-reduction methods as well as calibration methods aimed at following over time chemical experiments organized in batches. To illustrate it, this paper uses pharmaceutical data collected in a research and development context towards industrial production. This methodological framework aims to reach two objectives. The first objective is to visualize and interpret in real time, or off-line, the kinetics of chemical reactions using the following dimension-reduction methods: principal component analysis (PCA), non-negative matrix factorization (NMF) and multivariate curve resolution (MCR). The results show that, due to their additional constraints, NMF and MCR allow a better interpretability of chemical reactions than PCA with a comparable quality of fit. Moreover, eventough NMF and MCR come from different fields, their algorithms share many similarities and produce close results. The second objective is to predict chemical component concentrations over time. For this second objective, the partial least squares regression (PLSR) is used in a one-step approach and compared with a two-step approach combining multivariate regression with PCA, NMF or MCR. The results show that spectra or scores obtained from unsupervised approaches PCA, NMF or MCR can be used to predict concentrations of the main chemical compounds continuously over all the time of the reaction with a good precision and with a gain of interpretability. For both objectives, possible model validation indices are also discussed including a leave-one-batch-out approach

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

Tensor Based Tumor Tissue Type Differentiation Using Magnetic Resonance Spectroscopic Imaging

Magnetic resonance spectroscopic imaging (MRSI) has the potential to characterise different tissue types in brain tumors. Blind source separation techniques are used to extract the specific tissue profiles and their corresponding distribution from the MRSI data. A 3-dimensional MRSI tensor is constructed from in vivo 2D-MRSI data of individual tumor patients. Non-negative canonical polyadic decomposition (NCPD) with common factor in mode-1 and mode-2 and l(1) regularization on mode-3 is applied on the MRSI tensor to differentiate various tissue types. Initial in vivo study shows that NCPD has better performance in identifying tumor and necrotic tissue type in high grade glioma patients compared to previous matrix-based decompositions, such as non-negative matrix factorization and hierarchical non-negative matrix factorization.status: publishe

Canonical polyadic decomposition for tissue type differentiation using multi-parametric MRI in high-grade gliomas

© 2016 IEEE. In diagnosis and treatment planning of brain tumors, characterisation and localization of tissue plays an important role. Blind source separation techniques are generally employed to extract the tissue-specific profiles and its corresponding distribution from the multi-parametric MRI. A 3-dimensional tensor is constructed from in-vivo multiparametric MRI of high grade glioma patients. Constrained canonical polyadic decomposition (CPD) with common factor in mode-1 and mode-2 and l1 regularization on mode-3 is applied on the 3-dimensional multi-parametric tensor to characterize various tissue types. An initial in-vivo study shows that CPD has slightly better performance in identifying active tumor and the tumor core region in high-grade glioma patients compared to hierarchical non-negative matrix factorization.status: publishe

A semi-automated segmentation framework for MRI based brain tumor segmentation using regularized nonnegative matrix factorization

© 2016 IEEE. Segmentation plays an important role in the clinical management of brain tumors. Clinical practice would benefit from accurate and automated volumetric delineation of the tumor and its subcompartments. We present a semi-automated framework for brain tumor segmentation based on regularized nonnegative matrix factorization (NMF). L1-regularization is incorporated into the NMF objective function to promote spatial consistency and sparseness of the tissue abundance maps. The pathological sources are initialized through user-defined voxel selection. Knowledge about the spatial location of the selected voxels is combined with tissue adjacency constraints in a post-processing step to enhance segmentation quality. The method is applied to the BRATS 2013 Leaderboard dataset, consisting of publicly available multi-sequence MRI data of brain tumor patients. Our method performs well in comparison with state-of-the-art, in particular for the enhancing tumor region, for which we reach the highest Dice score among all participants.status: publishe