7 research outputs found
Development of registration methods for cardiovascular anatomy and function using advanced 3T MRI, 320-slice CT and PET imaging
Different medical imaging modalities provide complementary anatomical and
functional information. One increasingly important use of such information is in
the clinical management of cardiovascular disease. Multi-modality data is helping
improve diagnosis accuracy, and individualize treatment. The Clinical Research
Imaging Centre at the University of Edinburgh, has been involved in a number
of cardiovascular clinical trials using longitudinal computed tomography (CT) and
multi-parametric magnetic resonance (MR) imaging. The critical image processing
technique that combines the information from all these different datasets is known
as image registration, which is the topic of this thesis. Image registration, especially
multi-modality and multi-parametric registration, remains a challenging field in
medical image analysis. The new registration methods described in this work were
all developed in response to genuine challenges in on-going clinical studies. These
methods have been evaluated using data from these studies.
In order to gain an insight into the building blocks of image registration methods,
the thesis begins with a comprehensive literature review of state-of-the-art algorithms.
This is followed by a description of the first registration method I developed to help
track inflammation in aortic abdominal aneurysms. It registers multi-modality and
multi-parametric images, with new contrast agents. The registration framework uses a
semi-automatically generated region of interest around the aorta. The aorta is aligned
based on a combination of the centres of the regions of interest and intensity matching.
The method achieved sub-voxel accuracy.
The second clinical study involved cardiac data. The first framework failed to
register many of these datasets, because the cardiac data suffers from a common
artefact of magnetic resonance images, namely intensity inhomogeneity. Thus I
developed a new preprocessing technique that is able to correct the artefacts in the
functional data using data from the anatomical scans. The registration framework,
with this preprocessing step and new particle swarm optimizer, achieved significantly
improved registration results on the cardiac data, and was validated quantitatively
using neuro images from a clinical study of neonates. Although on average
the new framework achieved accurate results, when processing data corrupted
by severe artefacts and noise, premature convergence of the optimizer is still a
common problem. To overcome this, I invented a new optimization method, that
achieves more robust convergence by encoding prior knowledge of registration. The
registration results from this new registration-oriented optimizer are more accurate
than other general-purpose particle swarm optimization methods commonly applied
to registration problems.
In summary, this thesis describes a series of novel developments to an image
registration framework, aimed to improve accuracy, robustness and speed. The
resulting registration framework was applied to, and validated by, different types of
images taken from several ongoing clinical trials. In the future, this framework could
be extended to include more diverse transformation models, aided by new machine
learning techniques. It may also be applied to the registration of other types and
modalities of imaging data
Simulation, optimization and instrumentation of agricultural biogas plants
During the last two decades, the production of renewable energy by anaerobic digestion (AD) in biogas plants has become increasingly popular due to its applicability to a great variety of organic material from energy crops and animal waste to the organic fraction of Municipal Solid Waste (MSW), and to the relative simplicity of AD plant designs. Thus, a whole new biogas market emerged in Europe, which is strongly supported by European and national funding and remuneration schemes. Nevertheless, stable and efficient operation and control of biogas plants can be challenging, due to the high complexity of the biochemical AD process, varying substrate quality and a lack of reliable online instrumentation. In addition, governmental support for biogas plants will decrease in the long run and the substrate market will become highly competitive.
The principal aim of the research presented in this thesis is to achieve a substantial improvement in the operation of biogas plants. At first, a methodology for substrate inflow optimization of full-scale biogas plants is developed based on commonly measured process variables and using dynamic simulation models as well as computational intelligence (CI) methods. This methodology which is appliquable to a broad range of different biogas plants is then followed by an evaluation of existing online instrumentation for biogas plants and the development of a novel UV/vis spectroscopic online measurement system for volatile fatty acids. This new measurement system, which uses powerful machine learning techniques, provides a substantial improvement in online process monitoring for biogas plants.
The methodologies developed and results achieved in the areas of simulation and optimization were validated at a full-scale agricultural biogas plant showing that global optimization of the substrate inflow based on dynamic simulation models is able to improve the yearly profit of a biogas plant by up to 70%. Furthermore, the validation of the newly developed online measurement for VFA concentration at an industrial biogas plant showed that a measurement accuracy of 88% is possible using UV/vis spectroscopic probes
Advances in Evolutionary Algorithms
With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field
Studies in particle swarm optimization technique for global optimization.
Ph. D. University of KwaZulu-Natal, Durban 2013.Abstract available in the digital copy.Articles found within the main body of the thesis in the print version is found at the end of the thesis in the digital version
Anales del XIII Congreso Argentino de Ciencias de la Computaci贸n (CACIC)
Contenido:
Arquitecturas de computadoras
Sistemas embebidos
Arquitecturas orientadas a servicios (SOA)
Redes de comunicaciones
Redes heterog茅neas
Redes de Avanzada
Redes inal谩mbricas
Redes m贸viles
Redes activas
Administraci贸n y monitoreo de redes y servicios
Calidad de Servicio (QoS, SLAs)
Seguridad inform谩tica y autenticaci贸n, privacidad
Infraestructura para firma digital y certificados digitales
An谩lisis y detecci贸n de vulnerabilidades
Sistemas operativos
Sistemas P2P
Middleware
Infraestructura para grid
Servicios de integraci贸n (Web Services o .Net)Red de Universidades con Carreras en Inform谩tica (RedUNCI
Anales del XIII Congreso Argentino de Ciencias de la Computaci贸n (CACIC)
Contenido:
Arquitecturas de computadoras
Sistemas embebidos
Arquitecturas orientadas a servicios (SOA)
Redes de comunicaciones
Redes heterog茅neas
Redes de Avanzada
Redes inal谩mbricas
Redes m贸viles
Redes activas
Administraci贸n y monitoreo de redes y servicios
Calidad de Servicio (QoS, SLAs)
Seguridad inform谩tica y autenticaci贸n, privacidad
Infraestructura para firma digital y certificados digitales
An谩lisis y detecci贸n de vulnerabilidades
Sistemas operativos
Sistemas P2P
Middleware
Infraestructura para grid
Servicios de integraci贸n (Web Services o .Net)Red de Universidades con Carreras en Inform谩tica (RedUNCI