335 research outputs found

    Gauging Conformal Algebras with Relations between the Generators

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    We investigate the gauging of conformal algebras with relations between the generators. We treat the W5/2W_{5/2}--algebra as a specific example. We show that the gauge-algebra is in general reducible with an infinite number of stages. We show how to construct the BV-extended action, and hence the classical BRST charge. An important conclusion is that this can always be done in terms of the generators of the WW--algebra only, that is, independent of the realisation. The present treatment is still purely classical, but already enables us to learn more about reducible gauge algebras and the BV-formalism.Comment: 10 pages, LaTex, This paper is based on proceedings for the Sixth Seminar on Quantum Gravity, Moscow, 12-17 June '95 and the Conference on Gauge theories, Applied Supersymmetry and Quantum Gravity, Leuven, 10-14 July '9

    An Algorithmic Approach to Operator Product Expansions, WW-Algebras and WW-Strings

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    String theory is currently the most promising theory to explain the spectrum of the elementary particles and their interactions. One of its most important features is its large symmetry group, which contains the conformal transformations in two dimensions as a subgroup. At quantum level, the symmetry group of a theory gives rise to differential equations between correlation functions of observables. We show that these Ward-identities are equivalent to Operator Product Expansions (OPEs), which encode the short-distance singularities of correlation functions with symmetry generators. The OPEs allow us to determine algebraically many properties of the theory under study. We analyse the calculational rules for OPEs, give an algorithm to compute OPEs, and discuss an implementation in Mathematica. There exist different string theories, based on extensions of the conformal algebra to so-called W-algebras. These algebras are generically nonlinear. We study their OPEs, with as main results an efficient algorithm to compute the beta-coefficients in the OPEs, the first explicit construction of the WB_2-algebra, and criteria for the factorisation of free fields in a W-algebra. An important technique to construct realisations of W-algebras is Drinfel'd- Sokolov reduction. The method consists of imposing certain constraints on the elements of an affine Lie algebra. We quantise this reduction via gauged WZNW-models. This enables us in a theory with a gauged W-symmetry, to compute exactly the correlation functions of the effective theory. Finally, we investigate the critical W-string theories based on an extension of the conformal algebra with one symmetry generator of dimension N. We clarify how the spectrum of this theory forms a minimal model of the W_N-algebra.Comment: 127 pages, LaTex, shar-file including readme.txt, 12 latex files, 6 eps files and 6 pcx files, PhD. thesis KU Leuve

    Knitted ECG electrodes in relaxed fitting garments

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    A wide range of signal quality indices (SQIs) related to statistical methods, are used to guide the optimisation process of knitted textile electrodes for ECG recordings. The electrode structure and composition as well as their integration into a garment are evaluated in view of a fully knitted garment. The dry electrodes with the best SQIs are obtained by using conductive yarn only with a compact knit structure and a medium level of roughness. The best SQIs for the e-garment were obtained by sewing electrodes at their edges only, into the knitted garment. This implementation outperforms the intarsia and double-knit method as it allows the garment some independent movement from the electrodes, reducing motion artifacts. Tests done on a healthy volunteer demonstrate excellent system performance under gentle ambulation. The advantage of using SQIs in the optimisation process of dry textile ECG electrodes is that they offer a quantitative benchmark against which to compare other approaches. The fully knitted clothing allows for more relaxed e-garments when gentle ambulation is considered

    Surrogate-Driven Motion Model for Motion Compensated Cone-beam CT Reconstruction using Unsorted Projection Data

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    Cone-beam CT (CBCT) is widely used in image guided radiotherapy, but motion due to breathing can blur the image. Similar to 4DCT, 4D CBCT can reduce motion blur but 4D CBCT acquisitions take 2Ëś4 times longer than 3D CBCT and often suffer from phase sorting artefact. This study aims to obtain motion models and motion-free images simultaneously from unsorted 3D CBCT projection data, using a general motion modelling framework previously proposed by our group, which was for the first time successfully applied to real CBCT data equivalent to a one-minute acquisition. The performance of our method was comprehensively evaluated through digital phantom simulation and also validated on real patient data. This study demonstrated the feasibility of our proposed framework for simultaneous motion model fitting and motion compensated reconstruction using unsorted 3D CBCT projection data

    (An overview of) Synergistic reconstruction for multimodality/multichannel imaging methods

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    Imaging is omnipresent in modern society with imaging devices based on a zoo of physical principles, probing a specimen across different wavelengths, energies and time. Recent years have seen a change in the imaging landscape with more and more imaging devices combining that which previously was used separately. Motivated by these hardware developments, an ever increasing set of mathematical ideas is appearing regarding how data from different imaging modalities or channels can be synergistically combined in the image reconstruction process, exploiting structural and/or functional correlations between the multiple images. Here we review these developments, give pointers to important challenges and provide an outlook as to how the field may develop in the forthcoming years. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'

    Dendritic Cells: The Tools for Cancer Treatment

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    During cancer immune editing, the immune system shapes tumor fate in three phases through the activation of innate and adaptive immune mechanisms. After the elimination and equilibrium phase, the escape phase represents the final phase in which immunologically sculpted tumors begin to grow progressively. In this chapter, we will discuss which efforts are made to restore the balance in favor of the immune system making use of dendritic cells (DCs). The first approach is adoptive cell transfer, in which autologous DCs are generated and activated ex vivo. Secondly, we will discuss attempts in which pro-inflammatory or pro-migratory factors are delivered to attract and activate DCs in situ. Both strategies have the general goal to activate and mature DCs able to induce a robust tumor-specific T cell response. In addition, this chapter will discuss the clinical impact of DC-based therapies in cancer treatment focusing on the safety, feasibility, immunological responses, and clinical outcome

    Magnetic Resonance Fingerprinting with Total Nuclear Variation Regularisation

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    Magnetic Resonance Fingerprinting (MRF) accelerates quantitative magnetic resonance imaging. The reconstruction can be separated into two problems: reconstruction of a set of multi-contrast images from k-space signals, and estimation of parametric maps from the set of multi-contrast images. In this study we focus on the former problem, while leveraging dictionary matching for the estimation of parametric maps. Two different sparsity promoting regularisation strategies were investigated: contrast-wise Total Variation (TV) which encourages image sparsity separately; and Total Nuclear Variation (TNV) which promotes a measure of joint edge sparsity. We found improved results using joint sparsity

    Investigating Intensity Normalisation for PET Reconstruction with Supervised Deep Learning

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    Deep learning methods have shown great promise in the field of Positron Emission Tomography (PET) reconstruction, but the successful application of these methods depends heavily on the intensity scale of the images. Normalisation is a crucial step that aims to adjust the intensity of network inputs to make them more uniform and comparable across samples, acquisition times, and activity levels. In this work, we study the influence of different linear intensity normalisation approaches. We focus on two popular deep learning based image reconstruction methods: an unrolled algorithm (Learned Primal-Dual) and a post-processing method (OSEMConvNet). Results on the out-ofdistribution test dataset demonstrate that the choice of intensity normalisation significantly impacts on generalisability of these methods. Normalisation using the mean of acquisition data or corrected acquisition data led to improved peak-signal-to-noiseratio (PSNR) and data-consistency (KLDIV). Through evaluation of lesion-specific metrics of contrast recovery coefficients (CRC) and standard deviation (STD) an increase in CRC and STD is observed. These findings highlight the importance of carefully selecting an appropriate normalisation method for supervised deep learning-based PET reconstruction applications
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