Wavelets and sparse methods for image reconstruction and classification in neuroimaging

This dissertation contributes to neuroimaging literature in the fields of compressed sensing magnetic resonance imaging (CS-MRI) and image-based detection of Alzheimer’s disease (AD). It consists of three main contributions, based on wavelets and sparse methods. The first contribution is a method for wavelet packet basis optimisation for sparse approximation and compressed sensing reconstruction of magnetic resonance (MR) images of the brain. The proposed method is based on the basis search algorithm developed by Coifman and Wickerhauser, with a cost function designed specifically for compressed sensing. It is tested on MR images available from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The second contribution consists of evaluating and comparing several sparse classification methods in an application to detection of AD based on positron emission tomography (PET) images of the brain. This comparison includes univariate feature selection, feature clustering and classifiers that automatically select a small subset of features due to their mathematical or algorithmic construction. The evaluation is based on PET images available from ADNI. The third contribution is proposing an extension of wavelet-based scattering networks (originally proposed by Mallat and Bruna) to three-dimensional tomographic images. The proposed extension is evaluated as a feature representation in an application to detection of AD based on MR images available from ADNI. There are several possible extensions of the work presented in this dissertation. The wavelet packet basis search method proposed in the first contribution can be improved to take into account the coherence between the sparse approximation basis and the sensing basis. The evaluation presented in the second contribution can be extended with additional algorithms to make it more comprehensive. The three-dimensional scattering networks that are the core part of the third contribution can be combined with other machine learning methods, such as manifold learning or deep convolutional neural networks. As a whole, the methods proposed in this dissertation contribute to the work towards efficient screening for Alzheimer’s disease, by making MRI scans of the brain faster and helping to automate image analysis for AD detection. The first contribution is a method for wavelet packet basis optimisation for sparse approximation and compressed sensing reconstruction of magnetic resonance (MR) images of the brain. The proposed method is based on the basis search algorithm developed by Coifman and Wickerhauser, with a cost function designed specifically for compressed sensing. It is tested on MR images available from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The second contribution consists of evaluating and comparing several sparse classification methods in an application to detection of AD based on positron emission tomography (PET) images of the brain. This comparison includes univariate feature selection, feature clustering and classifiers that automatically select a small subset of features due to their mathematical or algorithmic construction. The evaluation is based on PET images available from ADNI. The third contribution is proposing an extension of wavelet-based scattering networks (originally proposed by Mallat and Bruna) to three-dimensional tomographic images. The proposed extension is evaluated as a feature representation in an application to detection of AD based on MR images available from ADNI. There are several possible extensions of the work presented in this dissertation. The wavelet packet basis search method proposed in the first contribution can be improved to take into account the coherence between the sparse approximation basis and the sensing basis. The evaluation presented in the second contribution can be extended with additional algorithms to make it more comprehensive. The three-dimensional scattering networks that are the core part of the third contribution can be combined with other machine learning methods, such as manifold learning or deep convolutional neural networks. This dissertation contributes to neuroimaging literature in the fields of compressed sensing magnetic resonance imaging (CS-MRI) and image-based detection of Alzheimer’s disease (AD). It consists of three main contributions, based on wavelets and sparse methods. The first contribution is a method for wavelet packet basis optimisation for sparse approximation and compressed sensing reconstruction of magnetic resonance (MR) images of the brain. The proposed method is based on the basis search algorithm developed by Coifman and Wickerhauser, with a cost function designed specifically for compressed sensing. It is tested on MR images available from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The second contribution consists of evaluating and comparing several sparse classification methods in an application to detection of AD based on positron emission tomography (PET) images of the brain. This comparison includes univariate feature selection, feature clustering and classifiers that automatically select a small subset of features due to their mathematical or algorithmic construction. The evaluation is based on PET images available from ADNI. The third contribution is proposing an extension of wavelet-based scattering networks (originally proposed by Mallat and Bruna) to three-dimensional tomographic images. The proposed extension is evaluated as a feature representation in an application to detection of AD based on MR images available from ADNI. There are several possible extensions of the work presented in this dissertation. The wavelet packet basis search method proposed in the first contribution can be improved to take into account the coherence between the sparse approximation basis and the sensing basis. The evaluation presented in the second contribution can be extended with additional algorithms to make it more comprehensive. The three-dimensional scattering networks that are the core part of the third contribution can be combined with other machine learning methods, such as manifold learning or deep convolutional neural networks. As a whole, the methods proposed in this dissertation contribute to the work towards efficient screening for Alzheimer’s disease, by making MRI scans of the brain faster and helping to automate image analysis for AD detection.Open Acces

The expression of Platelet-derived Growth factor receptors (PDGFRs) and their correlation with overall survival of patients with ovarian cancer

Objectives: The main aim of the study was to investigate the expression of Platelet-Derived Growth Factor Receptors alpha (PDGFR-alpha) and beta (PDGFR-beta) in malignant and benign ovarian tumors. We performed an analysis of the correlation of PDGFRs expression and stage of the disease, tumor grade and histopathological type of epithelial ovarian cancer (EOC). Additionally, we evaluated patient prognosis according to PDGFR expression.  Material and methods: Our study group was composed of 52 samples of EOCs, 35 samples of benign ovarian tumors (BOTs), and 21 samples of unchanged ovaries (UOs). The samples were collected from patients who had been operated on in the Division of Gynecological Surgery of the Poznan University of Medical Sciences.  Results: PDGFR-alpha was found to be expressed more frequently in cancer cells of EOCs, when compared with tumor cells of BOTs and epithelium of UOs. On the other hand, PDGFR-alpha receptors were present less frequently in the stroma of EOCs, when compared with the stroma of BOTs and UOs. Comparing the studied groups, there were no statistically significant differences in the expression of PDGFR-beta. The expression of both PDGFRs was not related to the FIGO stage, grade or histopathological type of EOCs. The expression of the PDGFR-beta receptor in cancer cells was associated with an improved overall survival among patients with EOCs. Patient prognosis was not affected by either PDGFR-alpha expres- sion or by PDGFR-beta tumor stroma expression.  Conclusions: The expression of PDGFR-alpha is significantly different when comparing EOCs, BOTs and UOs. However, the prognosis of EOC only seems to be affected by PDGFR-beta expression in cancer cells.

CMS physics technical design report : Addendum on high density QCD with heavy ions

Peer reviewe

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A concise overview of the laboratory solution of the FPGA based TESLA cavity simulator and controller (SIMCON) is presented. The major emphasis is put in this paper on the high level part of the system. There were described the following steps of the system design and realization: solution choice, design of system components, implementing the solutions, introduction of the application, initial analysis of the working application. The paper is a first description of the working DOOCS server for the FPGA based TESLA cavity SIMCON (which is a part of the LLRF subsystem). The data gathered from the work of the DOOCS server promise for the system optimization possibilities. The server will be supplemented with the GUI in the next step of this effort. Throughout the work we will refer to the debated system as to the TESLA SIMCON DOOCS server or in short the 'simcon server.' The hardware layer of the TESLA cavity SIMCON (to which the designed software refers to) was realized in a single FPGA Virtex chip by Xilinx (XC2V3000 development board by Nallatech)

Diversity and Horizontal Transfer of Antarctic Pseudomonas spp. Plasmids

Pseudomonas spp. are widely distributed in various environments around the world. They are also common in the Antarctic regions. To date, almost 200 plasmids of Pseudomonas spp. have been sequenced, but only 12 of them were isolated from psychrotolerant strains. In this study, 15 novel plasmids of cold-active Pseudomonas spp. originating from the King George Island (Antarctica) were characterized using a combined, structural and functional approach, including thorough genomic analyses, functional analyses of selected genetic modules, and identification of active transposable elements localized within the plasmids and comparative genomics. The analyses performed in this study increased the understanding of the horizontal transfer of plasmids found within Pseudomonas populations inhabiting Antarctic soils. It was shown that the majority of the studied plasmids are narrow-host-range replicons, whose transfer across taxonomic boundaries may be limited. Moreover, structural and functional analyses enabled identification and characterization of various accessory genetic modules, including genes encoding major pilin protein (PilA), that enhance biofilm formation, as well as active transposable elements. Furthermore, comparative genomic analyses revealed that the studied plasmids of Antarctic Pseudomonas spp. are unique, as they are highly dissimilar to the other known plasmids of Pseudomonas spp