391 research outputs found
Diagnostic tools for 3D unstructured oceanographic data
Most ocean models in current use are built upon structured meshes. It follows
that most existing tools for extracting diagnostic quantities (volume and
surface integrals, for example) from ocean model output are constructed using
techniques and software tools which assume structured meshes. The greater
complexity inherent in unstructured meshes (especially fully unstructured grids
which are unstructured in the vertical as well as the horizontal direction) has
left some oceanographers, accustomed to traditional methods, unclear on how to
calculate diagnostics on these meshes. In this paper we show that tools for
extracting diagnostic data from the new generation of unstructured ocean models
can be constructed with relative ease using open source software. Higher level
languages such as Python, in conjunction with packages such as NumPy, SciPy,
VTK and MayaVi, provide many of the high-level primitives needed to perform 3D
visualisation and evaluate diagnostic quantities, e.g. density fluxes. We
demonstrate this in the particular case of calculating flux of vector fields
through isosurfaces, using flow data obtained from the unstructured mesh finite
element ocean code ICOM, however this tool can be applied to model output from
any unstructured grid ocean code
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
Hypersweeps, Convective Clouds and Reeb Spaces
Isosurfaces are one of the most prominent tools in scientific data visualisation. An isosurface is a surface that defines the boundary of a feature of interest in space for a given threshold. This is integral in analysing data from the physical sciences which observe and simulate three or four dimensional phenomena. However it is time consuming and impractical to discover surfaces of interest by manually selecting different thresholds.
The systematic way to discover significant isosurfaces in data is with a topological data structure called the contour tree. The contour tree encodes the connectivity and shape of each isosurface at all possible thresholds. The first part of this work has been devoted to developing algorithms that use the contour tree to discover significant features in data using high performance computing systems. Those algorithms provided a clear speedup over previous methods and were used to visualise physical plasma simulations.
A major limitation of isosurfaces and contour trees is that they are only applicable when a single property is associated with data points. However scientific data sets often take multiple properties into account. A recent breakthrough generalised isosurfaces to fiber surfaces. Fiber surfaces define the boundary of a feature where the threshold is defined in terms of multiple parameters, instead of just one. In this work we used fiber surfaces together with isosurfaces and the contour tree to create a novel application that helps atmosphere scientists visualise convective cloud formation. Using this application, they were able to, for the first time, visualise the physical properties of certain structures that trigger cloud formation.
Contour trees can also be generalised to handle multiple parameters. The natural extension of the contour tree is called the Reeb space and it comes from the pure mathematical field of fiber topology. The Reeb space is not yet fully understood mathematically and algorithms for computing it have significant practical limitations. A key difficulty is that while the contour tree is a traditional one dimensional data structure made up of points and lines between them, the Reeb space is far more complex. The Reeb space is made up of two dimensional sheets, attached to each other in intricate ways. The last part of this work focuses on understanding the structure of Reeb spaces and the rules that are followed when sheets are combined. This theory builds towards developing robust combinatorial algorithms to compute and use Reeb spaces for practical data analysis
Visualisation of multi-dimensional medical images with application to brain electrical impedance tomography
Medical imaging plays an important role in modem medicine. With the increasing complexity and information presented by medical images, visualisation is vital for medical research and clinical applications to interpret the information presented in these images. The aim of this research is to investigate improvements to medical image visualisation, particularly for multi-dimensional medical image datasets. A recently
developed medical imaging technique known as Electrical Impedance Tomography (EIT) is presented as a demonstration. To fulfil the aim, three main efforts are included in this work.
First, a novel scheme for the processmg of brain EIT data with SPM (Statistical Parametric Mapping) to detect ROI (Regions of Interest) in the data is proposed based on a theoretical analysis. To evaluate the feasibility of this scheme, two types of experiments are carried out: one is implemented with simulated EIT data, and the other is performed with human brain EIT data under visual stimulation. The experimental
results demonstrate that: SPM is able to localise the expected ROI in EIT data correctly; and it is reasonable to use the balloon hemodynamic change model to simulate the
impedance change during brain function activity.
Secondly, to deal with the absence of human morphology information in EIT visualisation, an innovative landmark-based registration scheme is developed to register brain EIT image with a standard anatomical brain atlas.
Finally, a new task typology model is derived for task exploration in medical image visualisation, and a task-based system development methodology is proposed for the visualisation of multi-dimensional medical images. As a case study, a prototype visualisation system, named EIT5DVis, has been developed, following this methodology. to visualise five-dimensional brain EIT data. The EIT5DVis system is able to accept visualisation tasks through a graphical user interface; apply appropriate methods to analyse tasks, which include the ROI detection approach and registration scheme mentioned in the preceding paragraphs; and produce various visualisations
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