Dose reconstruction in the context of tomotherapy

In vivo dosimetry is a way to verify the radiation dose delivered to the patient in measuring the dose generally during the first fraction of the treatment. It is the only dose delivery control based on a measurement performed during the treatment. In today's radiotherapy practice, the dose delivered to the patient is planned using 3D dose calculation algorithms and volumetric images representing the patient. Due to the high accuracy and precision necessary in radiation treatments, national and international organisations like ICRU and AAPM recommend the use of in vivo dosimetry. It is also mandatory in some countries like France. Various in vivo dosimetry methods have been developed during the past years. These methods are point-, line-, plane- or 3D dose controls. A 3D in vivo dosimetry provides the most information about the dose delivered to the patient, with respect to ID and 2D methods. However, to our knowledge, it is generally not routinely applied to patient treatments yet. The aim of this PhD thesis was to determine whether it is possible to reconstruct the 3D delivered dose using transmitted beam measurements in the context of narrow beams. An iterative dose reconstruction method has been described and implemented. The iterative algorithm includes a simple 3D dose calculation algorithm based on the convolution/superposition principle. The methodology was applied to narrow beams produced by a conventional 6 MV linac. The transmitted dose was measured using an array of ion chambers, as to simulate the linear nature of a tomotherapy detector. We showed that the iterative algorithm converges quickly and reconstructs the dose within a good agreement (at least 3% / 3 mm locally), which is inside the 5% recommended by the ICRU. Moreover it was demonstrated on phantom measurements that the proposed method allows us detecting some set-up errors and interfraction geometry modifications. We also have discussed the limitations of the 3D dose reconstruction for dose delivery error detection. Afterwards, stability tests of the tomotherapy MVCT built-in onboard detector was performed in order to evaluate if such a detector is suitable for 3D in-vivo dosimetry. The detector showed stability on short and long terms comparable to other imaging devices as the EPIDs, also used for in vivo dosimetry. Subsequently, a methodology for the dose reconstruction using the tomotherapy MVCT detector is proposed in the context of static irradiations. This manuscript is composed of two articles and a script providing further information related to this work. In the latter, the first chapter introduces the state-of-the-art of in vivo dosimetry and adaptive radiotherapy, and explains why we are interested in performing 3D dose reconstructions. In chapter 2 a dose calculation algorithm implemented for this work is reviewed with a detailed description of the physical parameters needed for calculating 3D absorbed dose distributions. The tomotherapy MVCT detector used for transit measurements and its characteristics are described in chapter 3. Chapter 4 contains a first article entitled '3D dose reconstruction for narrow beams using ion chamber array measurements', which describes the dose reconstruction method and presents tests of the methodology on phantoms irradiated with 6 MV narrow photon beams. Chapter 5 contains a second article 'Stability of the Helical TomoTherapy HiArt II detector for treatment beam irradiations. A dose reconstruction process specific to the use of the tomotherapy MVCT detector is presented in chapter 6. A discussion and perspectives of the PhD thesis are presented in chapter 7, followed by a conclusion in chapter 8. The tomotherapy treatment device is described in appendix 1 and an overview of 3D conformai- and intensity modulated radiotherapy is presented in appendix 2. - La dosimétrie in vivo est une technique utilisée pour vérifier la dose délivrée au patient en faisant une mesure, généralement pendant la première séance du traitement. Il s'agit de la seule technique de contrôle de la dose délivrée basée sur une mesure réalisée durant l'irradiation du patient. La dose au patient est calculée au moyen d'algorithmes 3D utilisant des images volumétriques du patient. En raison de la haute précision nécessaire lors des traitements de radiothérapie, des organismes nationaux et internationaux tels que l'ICRU et l'AAPM recommandent l'utilisation de la dosimétrie in vivo, qui est devenue obligatoire dans certains pays dont la France. Diverses méthodes de dosimétrie in vivo existent. Elles peuvent être classées en dosimétrie ponctuelle, planaire ou tridimensionnelle. La dosimétrie 3D est celle qui fournit le plus d'information sur la dose délivrée. Cependant, à notre connaissance, elle n'est généralement pas appliquée dans la routine clinique. Le but de cette recherche était de déterminer s'il est possible de reconstruire la dose 3D délivrée en se basant sur des mesures de la dose transmise, dans le contexte des faisceaux étroits. Une méthode itérative de reconstruction de la dose a été décrite et implémentée. L'algorithme itératif contient un algorithme simple basé sur le principe de convolution/superposition pour le calcul de la dose. La dose transmise a été mesurée à l'aide d'une série de chambres à ionisations alignées afin de simuler la nature linéaire du détecteur de la tomothérapie. Nous avons montré que l'algorithme itératif converge rapidement et qu'il permet de reconstruire la dose délivrée avec une bonne précision (au moins 3 % localement / 3 mm). De plus, nous avons démontré que cette méthode permet de détecter certaines erreurs de positionnement du patient, ainsi que des modifications géométriques qui peuvent subvenir entre les séances de traitement. Nous avons discuté les limites de cette méthode pour la détection de certaines erreurs d'irradiation. Par la suite, des tests de stabilité du détecteur MVCT intégré à la tomothérapie ont été effectués, dans le but de déterminer si ce dernier peut être utilisé pour la dosimétrie in vivo. Ce détecteur a démontré une stabilité à court et à long terme comparable à d'autres détecteurs tels que les EPIDs également utilisés pour l'imagerie et la dosimétrie in vivo. Pour finir, une adaptation de la méthode de reconstruction de la dose a été proposée afin de pouvoir l'implémenter sur une installation de tomothérapie. Ce manuscrit est composé de deux articles et d'un script contenant des informations supplémentaires sur ce travail. Dans ce dernier, le premier chapitre introduit l'état de l'art de la dosimétrie in vivo et de la radiothérapie adaptative, et explique pourquoi nous nous intéressons à la reconstruction 3D de la dose délivrée. Dans le chapitre 2, l'algorithme 3D de calcul de dose implémenté pour ce travail est décrit, ainsi que les paramètres physiques principaux nécessaires pour le calcul de dose. Les caractéristiques du détecteur MVCT de la tomothérapie utilisé pour les mesures de transit sont décrites dans le chapitre 3. Le chapitre 4 contient un premier article intitulé '3D dose reconstruction for narrow beams using ion chamber array measurements', qui décrit la méthode de reconstruction et présente des tests de la méthodologie sur des fantômes irradiés avec des faisceaux étroits. Le chapitre 5 contient un second article intitulé 'Stability of the Helical TomoTherapy HiArt II detector for treatment beam irradiations'. Un procédé de reconstruction de la dose spécifique pour l'utilisation du détecteur MVCT de la tomothérapie est présenté au chapitre 6. Une discussion et les perspectives de la thèse de doctorat sont présentées au chapitre 7, suivies par une conclusion au chapitre 8. Le concept de la tomothérapie est exposé dans l'annexe 1. Pour finir, la radiothérapie «informationnelle 3D et la radiothérapie par modulation d'intensité sont présentées dans l'annexe 2.

Capacity-achieving input covariance for single-user multi-antenna channels

We characterize the capacity-achieving input covariance for multi-antenna channels known instantaneously at the receiver and in distribution at the transmitter. Our characterization, valid for arbitrary numbers of antennas, encompasses both the eigenvectors and the eigenvalues. The eigenvectors are found for zero-mean channels with arbitrary fading profiles and a wide range of correlation and keyhole structures. For the eigenvalues, in turn, we present necessary and sufficient conditions as well as an iterative algorithm that exhibits remarkable properties: universal applicability, robustness and rapid convergence. In addition, we identify channel structures for which an isotropic input achieves capacity.

3D dose reconstruction for narrow beams using ion chamber array measurements.

3D dose reconstruction is a verification of the delivered absorbed dose. Our aim was to describe and evaluate a 3D dose reconstruction method applied to phantoms in the context of narrow beams. A solid water phantom and a phantom containing a bone-equivalent material were irradiated on a 6 MV linac. The transmitted dose was measured by using one array of a 2D ion chamber detector. The dose reconstruction was obtained by an iterative algorithm. A phantom set-up error and organ interfraction motion were simulated to test the algorithm sensitivity. In all configurations convergence was obtained within three iterations. A local reconstructed dose agreement of at least 3% / 3mm with respect to the planned dose was obtained, except in a few points of the penumbra. The reconstructed primary fluences were consistent with the planned ones, which validates the whole reconstruction process. The results validate our method in a simple geometry and for narrow beams. The method is sensitive to a set-up error of a heterogeneous phantom and interfraction heterogeneous organ motion.

A novel semi-fragile forensic watermarking scheme for remote sensing images

Peer-reviewed

ML estimator and hybrid beamformer for multipath and interference mitigation in GNSS receivers

This paper addresses the estimation of the code-phase(pseudorange) and the carrier-phase of the direct signal received from a direct-sequence spread-spectrum satellite transmitter. Thesignal is received by an antenna array in a scenario with interferenceand multipath propagation. These two effects are generallythe limiting error sources in most high-precision positioning applications.A new estimator of the code- and carrier-phases is derivedby using a simplified signal model and the maximum likelihood(ML) principle. The simplified model consists essentially ofgathering all signals, except for the direct one, in a component withunknown spatial correlation. The estimator exploits the knowledgeof the direction-of-arrival of the direct signal and is much simplerthan other estimators derived under more detailed signal models.Moreover, we present an iterative algorithm, that is adequate for apractical implementation and explores an interesting link betweenthe ML estimator and a hybrid beamformer. The mean squarederror and bias of the new estimator are computed for a numberof scenarios and compared with those of other methods. The presentedestimator and the hybrid beamforming outperform the existingtechniques of comparable complexity and attains, in manysituations, the Cramér–Rao lower bound of the problem at hand.

OFDM joint data detection and phase noise cancellation based on minimum mean square prediction error

This paper proposes a new iterative algorithm for orthogonal frequency division multiplexing (OFDM) joint data detection and phase noise (PHN) cancellation based on minimum mean square prediction error. We particularly highlight the relatively less studied problem of "overfitting" such that the iterative approach may converge to a trivial solution. Specifically, we apply a hard-decision procedure at every iterative step to overcome the overfitting. Moreover, compared with existing algorithms, a more accurate Pade approximation is used to represent the PHN, and finally a more robust and compact fast process based on Givens rotation is proposed to reduce the complexity to a practical level. Numerical Simulations are also given to verify the proposed algorithm. (C) 2008 Elsevier B.V. All rights reserved.

A simple analysis of thermodynamic properties for classical plasmas: I. theory

By eliminating the short range negative divergence of the Debye–Hückel pair distribution function, but retaining the exponential charge screening known to operate at large interparticle separation, the thermodynamic properties of one-component plasmas of point ions or charged hard spheres can be well represented even in the strong coupling regime. Predicted electrostatic free energies agree within 5% of simulation data for typical Coulomb interactions up to a factor of 10 times the average kinetic energy. Here, this idea is extended to the general case of a uniform ionic mixture, comprising an arbitrary number of components, embedded in a rigid neutralizing background. The new theory is implemented in two ways: (i) by an unambiguous iterative algorithm that requires numerical methods and breaks the symmetry of cross correlation functions; and (ii) by invoking generalized matrix inverses that maintain symmetry and yield completely analytic solutions, but which are not uniquely determined. The extreme computational simplicity of the theory is attractive when considering applications to complex inhomogeneous fluids of charged particles.

Influence diagnostics in nonlinear mixed-effects elliptical models

In this work we propose and analyze nonlinear elliptical models for longitudinal data, which represent an alternative to gaussian models in the cases of heavy tails, for instance. The elliptical distributions may help to control the influence of the observations in the parameter estimates by naturally attributing different weights for each case. We consider random effects to introduce the within-group correlation and work with the marginal model without requiring numerical integration. An iterative algorithm to obtain maximum likelihood estimates for the parameters is presented, as well as diagnostic results based on residual distances and local influence [Cook, D., 1986. Assessment of local influence. journal of the Royal Statistical Society - Series B 48 (2), 133-169; Cook D., 1987. Influence assessment. journal of Applied Statistics 14 (2),117-131; Escobar, L.A., Meeker, W.Q., 1992, Assessing influence in regression analysis with censored data, Biometrics 48, 507-528]. As numerical illustration, we apply the obtained results to a kinetics longitudinal data set presented in [Vonesh, E.F., Carter, R.L., 1992. Mixed-effects nonlinear regression for unbalanced repeated measures. Biometrics 48, 1-17], which was analyzed under the assumption of normality. (C) 2009 Elsevier B.V. All rights reserved.

Computational study of the step size parameter of the subgradient optimization method

The subgradient optimization method is a simple and flexible linear programming iterative algorithm. It is much simpler than Newton's method and can be applied to a wider variety of problems. It also converges when the objective function is non-differentiable. Since an efficient algorithm will not only produce a good solution but also take less computing time, we always prefer a simpler algorithm with high quality. In this study a series of step size parameters in the subgradient equation is studied. The performance is compared for a general piecewise function and a specific p-median problem. We examine how the quality of solution changes by setting five forms of step size parameter.

Data mining por meio de análise de redes, no contexto de filtro colaborativo

Tendo como motivação o desenvolvimento de uma representação gráfica de redes com grande número de vértices, útil para aplicações de filtro colaborativo, este trabalho propõe a utilização de superfícies de coesão sobre uma base temática multidimensionalmente escalonada. Para isso, utiliza uma combinação de escalonamento multidimensional clássico e análise de procrustes, em algoritmo iterativo que encaminha soluções parciais, depois combinadas numa solução global. Aplicado a um exemplo de transações de empréstimo de livros pela Biblioteca Karl A. Boedecker, o algoritmo proposto produz saídas interpretáveis e coerentes tematicamente, e apresenta um stress menor que a solução por escalonamento clássico.

Uma implementação da análise de componentes independentes em plataforma de hardware reconfigurável

Blind Source Separation (BSS) refers to the problem of estimate original signals from observed linear mixtures with no knowledge about the sources or the mixing process. Independent Component Analysis (ICA) is a technique mainly applied to BSS problem and from the algorithms that implement this technique, FastICA is a high performance iterative algorithm of low computacional cost that uses nongaussianity measures based on high order statistics to estimate the original sources. The great number of applications where ICA has been found useful reects the need of the implementation of this technique in hardware and the natural paralelism of FastICA favors the implementation of this algorithm on digital hardware. This work proposes the implementation of FastICA on a reconfigurable hardware platform for the viability of it's use in blind source separation problems, more specifically in a hardware prototype embedded in a Field Programmable Gate Array (FPGA) board for the monitoring of beds in hospital environments. The implementations will be carried out by Simulink models and it's synthesizing will be done through the DSP Builder software from Altera Corporation.

Otimização de um sistema de distribuição predial de água fria: estudo de caso

This dissertation presents a methodology to the optimization of a predial system of cold water distribution. It s about a study of a case applied to the Tropical Buzios Residential Condominium, located in the Búzio s Beach, Nísia Floresta city, the east coast of the Rio Grande do Norte state, twenty kilometers far from Natal. The design of cold water distribution networks according to Norm NBR 5626 of the ABNT - Brazilian Association of Techniques Norms, does not guarantee that the joined solution is the optimal solution of less cost. It s necessary the use of an optimization methodology, that supplies us, between all the possible solutions, the minimum cost solution. In the optimization process of the predial system of water distribution of the Tropical Búzios Condominium, is used Method Granados, that is an iterative algorithm of optimization, based on the Dynamic Programming, that supplies the minimum cost s network, in function of the piezometric quota of the reservoir. For the application of this Method in ramifies networks, is used a program of computer in C language. This process is divided in two stages: attainment of the previous solution and reduction of the piezometric quota of headboard. In the attainment of the previous solution, the minors possible diameters are used that guarantee the limit of maximum speed and the requirements of minimum pressures. The piezometric quota of headboard is raised to guarantee these requirements. In the second stage of the Granados Method, an iterative process is used and it objective is to reduce the quota of headboard gradually, considering the substitution of stretches of the network pipes for the subsequent diameters, considering a minimum addition of the network cost. The diameter change is made in the optimal stretch that presents the lesser Exchange Gradient. The process is locked up when the headboard quota of desired is reached. The optimized network s material costs are calculated, and is made the analysis of the same ones, through the comparison with the conventional network s costs

Método de Projeções Ortogonais

The problem treated in this dissertation is to establish boundedness for the iterates of an iterative algorithm in 2, under some technical conditions. However, this paper uses non-trivial intuitive arguments and its proofs lack suficient rigor. In this dissertation we discuss and strengthen the results of this paper, in order to complete and simplify its proofs

Applications of Linear Programming to Coding Theory

Maximum-likelihood decoding is often the optimal decoding rule one can use, but it is very costly to implement in a general setting. Much effort has therefore been dedicated to find efficient decoding algorithms that either achieve or approximate the error-correcting performance of the maximum-likelihood decoder. This dissertation examines two approaches to this problem. In 2003 Feldman and his collaborators defined the linear programming decoder, which operates by solving a linear programming relaxation of the maximum-likelihood decoding problem. As with many modern decoding algorithms, is possible for the linear programming decoder to output vectors that do not correspond to codewords; such vectors are known as pseudocodewords. In this work, we completely classify the set of linear programming pseudocodewords for the family of cycle codes. For the case of the binary symmetric channel, another approximation of maximum-likelihood decoding was introduced by Omura in 1972. This decoder employs an iterative algorithm whose behavior closely mimics that of the simplex algorithm. We generalize Omura's decoder to operate on any binary-input memoryless channel, thus obtaining a soft-decision decoding algorithm. Further, we prove that the probability of the generalized algorithm returning the maximum-likelihood codeword approaches 1 as the number of iterations goes to infinity.

Influence diagnostics in heteroscedastic and/or autoregressive nonlinear elliptical models for correlated data

In this paper, we propose nonlinear elliptical models for correlated data with heteroscedastic and/or autoregressive structures. Our aim is to extend the models proposed by Russo et al. [22] by considering a more sophisticated scale structure to deal with variations in data dispersion and/or a possible autocorrelation among measurements taken throughout the same experimental unit. Moreover, to avoid the possible influence of outlying observations or to take into account the non-normal symmetric tails of the data, we assume elliptical contours for the joint distribution of random effects and errors, which allows us to attribute different weights to the observations. We propose an iterative algorithm to obtain the maximum-likelihood estimates for the parameters and derive the local influence curvatures for some specific perturbation schemes. The motivation for this work comes from a pharmacokinetic indomethacin data set, which was analysed previously by Bocheng and Xuping [1] under normality.