Estimación de la conductividad eléctrica del tejido humano en la terapia de hipertermia por radiofrecuencia

The use of mathematical models to study complex systems such as physical and biological phenomena allows understanding their behavior, specifically regarding variables and parameters that are difficult to obtain. Additionally, studying optimization techniques has made it possible to approximate the characteristics of these systems by correlating numerical simulations and experimentation. Radiofrequency hyperthermia therapy for cancer treatment is currently under consideration for future medical applications. However, some of its properties are complex to measure, which could prevent their control. This is the case of electrical conductivity, which depends on the induction frequency and the tissue characteristics. In this paper, radiofrequency hyperthermia therapy was simulated via the finite element method. Then, an estimation of the electrical conductivity involved in the treatment was performed using the particle swarm optimization method. The execution time and the difference between the estimated parameter and the exact value were evaluated and compared with those obtained using the Levenberg-Marquardt method. The results indicate a significant agreement between the estimated and exact values in three different cases. The Levenberg-Marquardt method has a difference of 0,1942% and a performance time of 22 minutes, whereas the particle swarm optimization method has a difference of 0,0967% and a performance time of 327 minutes. The latter performs better in terms of parameter value estimation, whereas the former has better computational times. These techniques may help medical doctors to prescribe treatment protocols and may open the possibility of devising control strategies for hyperthermia therapy as a cancer treatment.El uso de modelos matemáticos para el estudio de sistemas complejos como los fenómenos físicos y biológicos permite comprender su comportamiento, específicamente con respecto a variables y parámetros difíciles de obtener. Adicionalmente, el estudio de técnicas de optimización ha permitido aproximar las características de estos sistemas por medio de la correlación de simulaciones numéricas y la experimentación. La terapia de hipertermia por radiofrecuencia para el tratamiento del cáncer está actualmente en consideración para su futura aplicación médica. Sin embargo, algunas de sus propiedades son difíciles de medir, lo cual impediría su control. Este es el caso de la conductividad eléctrica, que depende de la frecuencia de inducción y de las características del tejido. En este artículo se simuló la terapia de hipertermia por radiofrecuencia mediante el método de elementos finitos. Luego se realizó una estimación de la conductividad eléctrica en el tratamiento mediante el método de optimización por enjambres de partículas. Se evaluaron el tiempo de ejecución y la diferencia del valor estimado con respecto al valor exacto, y se compararon sus valores estimados con los obtenidos mediante el método de Levenberg-Marquardt. Los resultados indican una concordancia significativa entre los valores estimados y los exactos en tres casos diferentes. El método de Levenberg-Marquardt tiene una diferencia de 0,1942% y un tiempo de ejecución de 22 minutos, mientras que el método de optimización de enjambres de partículas tiene una diferencia de 0,0967% y un tiempo de ejecución de 327 minutos. Este último tiene un mejor rendimiento en términos de estimación del valor de los parámetros, mientras que el otro tiene un mejor tiempo de ejecución computacional. Estas técnicas podrían ayudar a los médicos a prescribir protocolos de tratamiento y abrir la posibilidad de diseñar estrategias de control para la terapia de hipertermia como tratamiento para el cáncer.Fil: Lopez Perez, Jorge Iván. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Santiago de Cali; ColombiaFil: Bermeo Varón, L. A.. Universidad Santiago de Cali; Colombi

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Data-driven modelling of biological multi-scale processes

Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and Multiscale Dynamics (American Scientific Publishers

Nature’s Optics and Our Understanding of Light

Optical phenomena visible to everyone abundantly illustrate important ideas in science and mathematics. The phenomena considered include rainbows, sparkling reflections on water, green flashes, earthlight on the moon, glories, daylight, crystals, and the squint moon. The concepts include refraction, wave interference, numerical experiments, asymptotics, Regge poles, polarisation singularities, conical intersections, and visual illusions

Radio wave imaging using Ultra-Wide Band Spectrum Antennas for Near-Field Applications. Design, Development, and Measurements of Ultra-Wideband Antenna for Microwave Near-Field Imaging Applications by applying Optimisation Algorithms

The emergence of Ultra-wideband (UWB) technology application has yielded tremendous and vital impacts in the field of microwave wireless communications. These applications include military radar imaging, security screening, and tumour detection, especially for early detection of breast cancer. These indicators have stimulated and inspired many researchers to make the best use of this promising technology. UWB technology challenges such as antenna design, the problem of imaging reconstruction techniques, challenges of severe signal attenuation and dispersion in high loss material. Others are lengthy computational time demand and large computer memory requirements are prevalent constraints that need to be tackled especially in a large scale and complex computational electromagnetic analysis. In this regard, it is necessary to find out recently developed optimisation techniques that can provide solutions to these problems. In this thesis, designing, optimisation, development, measurement, and analysis of UWB antennas for near-field microwave imaging applications are considered. This technology emulates the same concept of surface penetrating radar operating in various forms of the UWB spectrum. The initial design of UWB monopole antennas, including T-slots, rectangular slots, and hexagonal slots on a circular radiating patch, was explicitly implemented for medical imaging applications to cover the UWB frequency ranging from 3.1 GHz to 10.6 GHz. Based on this concept, a new bow-tie and Vivaldi UWB antennas were designed for a through-the-wall imaging application. The new antennas were designed to cover a spectrum on a lower frequency ranging from 1 GHz - 4 GHz to ease the high wall losses that will be encountered when using a higher frequency range and to guarantee deeper penetration of the electromagnetic wave. Finally, both simulated and calculated results of the designed, optimised antennas indicate excellent agreement with improved performance in terms of return loss, gain, radiation pattern, and fidelity over the entire UWB frequency. These breakthroughs provided reduced computational time and computer memory requirement for useful, efficient, reliable, and compact sensors for imaging applications, including security and breast cancer detection, thereby saving more lives.Tertiary Education Trust Fund (TET Fund) Supported by the Nigerian Defence Academy (NDA

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Proof-of-Concept For Converging Beam Small Animal Irradiator

The Monte Carlo particle simulator TOPAS, the multiphysics solver COMSOL., and several analytical radiation transport methods were employed to perform an in-depth proof-ofconcept for a high dose rate, high precision converging beam small animal irradiation platform. In the first aim of this work, a novel carbon nanotube-based compact X-ray tube optimized for high output and high directionality was designed and characterized. In the second aim, an optimization algorithm was developed to customize a collimator geometry for this unique Xray source to simultaneously maximize the irradiator’s intensity and precision. Then, a full converging beam irradiator apparatus was fit with a multitude of these X-ray tubes in a spherical array and designed to deliver converged dose spots to any location within a small animal model. This aim also included dose leakage calculations for estimation of appropriate external shielding. The result of this research will be the blueprints for a full preclinical radiation platform that pushes the boundaries of dose localization in small animal trials

Modeling Human Atrial Patho-Electrophysiology from Ion Channels to ECG - Substrates, Pharmacology, Vulnerability, and P-Waves

Half of the patients suffering from atrial fibrillation (AF) cannot be treated adequately, today. This thesis presents multi-scale computational methods to advance our understanding of patho-mechanisms, to improve the diagnosis of patients harboring an arrhythmogenic substrate, and to tailor therapy. The modeling pipeline ranges from ion channels on the subcellular level up to the ECG on the body surface. The tailored therapeutic approaches carry the potential to reduce the burden of AF