Information
- Publication Type: PhD-Thesis
- Workgroup(s)/Project(s): not specified
- Date: October 2009
- Date (End): 2009
- Rigorosum: 27. November 2009
- First Supervisor:
Abstract
Recent advances in image acquisition technology and its availability in the medical
and bio-medical fields have lead to an unprecedented amount of high-resolution
imaging data. However, the inherent complexity of this data, caused by its
tremendous size, complex structure or multi-modality poses several challenges
for current visualization tools. Recent developments in graphics hardware architecture
have increased the versatility and processing power of today’s GPUs to
the point where GPUs can be considered parallel scientific computing devices.
The work in this thesis builds on the current progress in image acquisition
techniques and graphics hardware architecture to develop novel 3D visualization
methods for the fields of neurosurgery and neuroscience.
The first part of this thesis presents an application and framework for planning
of neurosurgical interventions. Concurrent GPU-based multi-volume rendering
is used to visualize multiple radiological imaging modalities, delineating
the patient’s anatomy, neurological function, and metabolic processes. Additionally,
novel interaction metaphors are introduced, allowing the surgeon to plan
and simulate the surgial approach to the brain based on the individual patient
anatomy.
The second part of this thesis focuses on GPU-based volume rendering techniques
for large and complex EM data, as required in the field of neuroscience.
A new mixed-resolution volume ray-casting approach is presented, which circumvents
artifacts at block boundaries of different resolutions. NeuroTrace is introduced,
an application for interactive segmentation and visualization of neural
processes in EM data. EM data is extremely dense, heavily textured and exhibits
a complex structure of interconnected nerve cells, making it difficult to achieve
high-quality volume renderings. Therefore, this thesis presents a novel on-demand
nonlinear noise removal and edge detection method which allows to enhance important
structures (e.g., myelinated axons) while de-emphasizing less important
regions of the data. In addition to the methods and concepts described above,
this thesis tries to bridge the gap between state-of-the-art visualization research
and the use of those visualization methods in actual medical and bio-medical
applications.
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BibTeX
@phdthesis{beyer-2009-gpu,
title = "GPU-based Multi-Volume Rendering of Complex Data in
Neuroscience and Neurosurgery",
author = "Johanna Beyer",
year = "2009",
abstract = "Recent advances in image acquisition technology and its
availability in the medical and bio-medical fields have lead
to an unprecedented amount of high-resolution imaging data.
However, the inherent complexity of this data, caused by its
tremendous size, complex structure or multi-modality poses
several challenges for current visualization tools. Recent
developments in graphics hardware architecture have
increased the versatility and processing power of today’s
GPUs to the point where GPUs can be considered parallel
scientific computing devices. The work in this thesis builds
on the current progress in image acquisition techniques and
graphics hardware architecture to develop novel 3D
visualization methods for the fields of neurosurgery and
neuroscience. The first part of this thesis presents an
application and framework for planning of neurosurgical
interventions. Concurrent GPU-based multi-volume rendering
is used to visualize multiple radiological imaging
modalities, delineating the patient’s anatomy,
neurological function, and metabolic processes.
Additionally, novel interaction metaphors are introduced,
allowing the surgeon to plan and simulate the surgial
approach to the brain based on the individual patient
anatomy. The second part of this thesis focuses on GPU-based
volume rendering techniques for large and complex EM data,
as required in the field of neuroscience. A new
mixed-resolution volume ray-casting approach is presented,
which circumvents artifacts at block boundaries of different
resolutions. NeuroTrace is introduced, an application for
interactive segmentation and visualization of neural
processes in EM data. EM data is extremely dense, heavily
textured and exhibits a complex structure of interconnected
nerve cells, making it difficult to achieve high-quality
volume renderings. Therefore, this thesis presents a novel
on-demand nonlinear noise removal and edge detection method
which allows to enhance important structures (e.g.,
myelinated axons) while de-emphasizing less important
regions of the data. In addition to the methods and concepts
described above, this thesis tries to bridge the gap between
state-of-the-art visualization research and the use of those
visualization methods in actual medical and bio-medical
applications.",
month = oct,
address = "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
school = "Institute of Computer Graphics and Algorithms, Vienna
University of Technology ",
URL = "https://www.cg.tuwien.ac.at/research/publications/2009/beyer-2009-gpu/",
}