2827

GPU-based Multi-Volume Rendering of Complex Data in Neuroscience and Neurosurgery

Johanna Beyer
Institute of Computer Graphics and Algorithms, Vienna University of Technology, Favoritenstrasse 9-11/186, A-1040 Vienna, Austria
Ph.D. thesis, Institute of Computer Graphics and Algorithms, Vienna University of Technology, 2009

@phdthesis{beyergpu,

   title={GPU-based Multi-Volume Rendering of Complex Data in Neuroscience and Neurosurgery},

   author={Beyer, J.},

   school={Ph. D. thesis, Institute of Computer Graphics and Algorithms, Vienna University of Technology, Favoritenstrasse 9-11/186, A-1040 Vienna, Austria (Oct. 2009).},

   URL={http://www.cg.tuwien.ac.at/research/publications/2009/beyer-2009-gpu}

}

Download Download (PDF)   View View   Source Source   

571

views

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.
No votes yet.
Please wait...

* * *

* * *

Featured events

2018
November
27-30
Hida Takayama, Japan

The Third International Workshop on GPU Computing and AI (GCA), 2018

2018
September
19-21
Nagoya University, Japan

The 5th International Conference on Power and Energy Systems Engineering (CPESE), 2018

2018
September
22-24
MediaCityUK, Salford Quays, Greater Manchester, England

The 10th International Conference on Information Management and Engineering (ICIME), 2018

2018
August
21-23
No. 1037, Luoyu Road, Hongshan District, Wuhan, China

The 4th International Conference on Control Science and Systems Engineering (ICCSSE), 2018

2018
October
29-31
Nanyang Executive Centre in Nanyang Technological University, Singapore

The 2018 International Conference on Cloud Computing and Internet of Things (CCIOT’18), 2018

HGPU group © 2010-2018 hgpu.org

All rights belong to the respective authors

Contact us: