97 research outputs found
Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial
Este documento contiene el proyecto docente e investigador del candidato Germán Moltó MartÃnez presentado como requisito para el concurso de acceso a plazas de Cuerpos Docentes Universitarios. Concretamente, el documento se centra en el concurso para la plaza 6708 de Catedrático de Universidad en el área de Ciencia de la Computación en el Departamento de Sistemas Informáticos y Computación de la Universitat Politécnica de València. La plaza está adscrita a la Escola Técnica Superior d'Enginyeria Informà tica y tiene como perfil las asignaturas "Infraestructuras de Cloud Público" y "Estructuras de Datos y Algoritmos".También se incluye el Historial Académico, Docente e Investigador, asà como la presentación usada durante la defensa.Germán Moltó MartÃnez (2022). Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial. http://hdl.handle.net/10251/18903
DICOM for EIT
With EIT starting to be used in routine clinical practice [1], it important that the clinically relevant information is portable between hospital data management systems. DICOM formats are widely used clinically and cover many imaging modalities, though not specifically EIT. We describe how existing DICOM specifications, can be repurposed as an interim solution, and basis from which a consensus EIT DICOM ‘Supplement’ (an extension to the standard) can be writte
Estimation of thorax shape for forward modelling in lungs EIT
The thorax models for pre-term babies are developed based on the CT scans from new-borns and their effect on image reconstruction is evaluated in comparison with other available models
Rapid generation of subject-specific thorax forward models
For real-time monitoring of lung function using accurate patient geometry, shape information needs to be acquired and a forward model generated rapidly. This paper shows that warping a cylindrical model to an acquired shape results in meshes of acceptable mesh quality, in terms of stretch and aspect ratio
Torso shape detection to improve lung monitoring
Two methodologies are proposed to detect the patient-specific boundary of the chest, aiming to produce a more accurate forward model for EIT analysis. Thus, a passive resistive and an inertial prototypes were prepared to characterize and reconstruct the shape of multiple phantoms. Preliminary results show how the passive device generates a minimum scatter between the reconstructed image and the actual shap
Nanoparticle electrical impedance tomography
We have developed a new approach to imaging with electrical impedance tomography (EIT) using gold nanoparticles (AuNPs) to enhance impedance changes at targeted tissue sites. This is achieved using radio frequency (RF) to heat nanoparticles while applying EIT imaging. The initial results using 5-nm citrate coated AuNPs show that heating can enhance the impedance in a solution containing AuNPs due to the application of an RF field at 2.60 GHz
Lattice-Boltzmann simulations of cerebral blood flow
Computational haemodynamics play a central role in the understanding of blood behaviour
in the cerebral vasculature, increasing our knowledge in the onset of vascular
diseases and their progression, improving diagnosis and ultimately providing better
patient prognosis. Computer simulations hold the potential of accurately characterising
motion of blood and its interaction with the vessel wall, providing the capability to
assess surgical treatments with no danger to the patient. These aspects considerably
contribute to better understand of blood circulation processes as well as to augment
pre-treatment planning. Existing software environments for treatment planning consist
of several stages, each requiring significant user interaction and processing time,
significantly limiting their use in clinical scenarios.
The aim of this PhD is to provide clinicians and researchers with a tool to aid
in the understanding of human cerebral haemodynamics. This tool employs a high
performance
fluid solver based on the lattice-Boltzmann method (coined HemeLB),
high performance distributed computing and grid computing, and various advanced
software applications useful to efficiently set up and run patient-specific simulations.
A graphical tool is used to segment the vasculature from patient-specific CT or MR
data and configure boundary conditions with ease, creating models of the vasculature
in real time. Blood flow visualisation is done in real time using in situ rendering
techniques implemented within the parallel
fluid solver and aided by steering capabilities;
these programming strategies allows the clinician to interactively display the
simulation results on a local workstation. A separate software application is used
to numerically compare simulation results carried out at different spatial resolutions,
providing a strategy to approach numerical validation. This developed software and
supporting computational infrastructure was used to study various patient-specific
intracranial aneurysms with the collaborating interventionalists at the National Hospital
for Neurology and Neuroscience (London), using three-dimensional rotational
angiography data to define the patient-specific vasculature. Blood flow motion was
depicted in detail by the visualisation capabilities, clearly showing vortex fluid
ow features and stress distribution at the inner surface of the aneurysms and their surrounding
vasculature. These investigations permitted the clinicians to rapidly assess
the risk associated with the growth and rupture of each aneurysm. The ultimate goal
of this work is to aid clinical practice with an efficient easy-to-use toolkit for real-time
decision support
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