Tensor Field Visualization for MRI Data
From BSV-Website
Contents |
Summary
In medical imaging, diffusion weighted magnetic resonance imaging (DW-MRI) is a modality capable of measuring the movement of hydrogen nuclei in vivo. Therefore, in contrast to computer tomography (CT) and magnetic resonance imaging (MRI), directional information can be extracted from the data. To date, diffusion tensors are the most common model to describe this directional information that is known to relate to the directions of, e.g., neural fibers or muscular structures.
The aim of this project is the definition of models to improve the representation of measured MRI data and their visualization.
Duration
2004 - 2008
Funding:
University of Leipzig
Research assistants
- Dr. Mario Hlawitschka
Publications
- Mario Hlawitschka, Sebastian Eichelbaum, Gerik Scheuermann. Fast and Memory Efficient GPU-based Rendering of Tensor Data. In Yingcai Xiao and Eleonore ten Thij, editors, Proceedings of the IADIS International Conference on Computer Graphics and Visualization, pages 36-42. 2008.
Graphics hardware is advancing very fast and offers new possibilities to programmers. The new features can be used in scientific visualization to move calculations from the CPU to the graphics processing unit (GPU). This is useful especially when mixing CPU intense calculations with on the fly visualization of intermediate results. We present a method to display a large amount of superquadric glyphs and demonstrate its use for visualization of measured second-order tensor data in diffusion tensor imaging (DTI) and to stress and strain tensors of computational fluid dynamic and material simulations.
- Mario Hlawitschka, Gunther H. Weber, Alfred Anwander, Owen T. Carmichael, Bernd Hamann, Gerik Scheuermann. Interactive Volume Rendering of Diffusion Tensor Data. In David H. Laidlaw and Joachim Weickert (Eds.) ,Visualization and Processing of Tensor Fields: Advances and Perspectives. Mathematics and Visualization(17). Springer, Berlin, 2009.
As 3D volumetric images of the human body become an increasingly crucial source of information for the diagnosis and treatment of a broad variety of medical conditions, advanced techniques that allow clinicians to efficiently and clearly visualize volumetric images become increasingly important. Interaction has proven to be a key concept in analysis of medical images because static images of 3D data are prone to artifacts and misunderstanding of depth. Furthermore, fading out clinically irrelevant aspects of the image while preserving contextual anatomical landmarks helps medical doctors to focus on important parts of the images without becoming disoriented. Our aim was to develop a tool that unifies interactive manipulation and context preserving visualization of medical images with a special focus on diffusion tensor imaging (DTI) data.
- Mario Hlawitschka and Gerik Scheuermann. Estimation and Visualization of Thermal Noise in HARDI Data. Technical Report, University of Leipzig, Germany.
Understanding the human brain, how it works and how we can cure diseases is one of the major goals of neuroscientists and surgeons. \ Diffusion weighted magnetic resonance tomography is an imaging modality measuring Brownian motion of particles that can be analyzed using q--Ball imaging. \ We analyze the noise present in measured MR images and track its influence through the processing steps to the final q--Ball data. To the best of our knowledge, for the first time in literature, a noise model is derived for q--Ball imaging directly in spherical harmonic representation and an algorithm for efficient calculation is presented. \ Finally, we visualize the influence of noise in a way that can be used in fiber tracking algorithms on q--Ball data.
- Mario Hlawitschka, Gerik Scheuermann, Bernd Hamann. Interactive Glyph Placement for Tensor Fields. In Proceedings of Third International Symposium on Visual Computing (ISVC 07), pages 331-340. Springer, 2007.
Visualization of glyphs has a long history in medical imaging but gains much more power when the glyphs are properly placed to fill the screen. Glyph packing is often performed via an iterative approach to improve the location of glyphs. We present an alternative implementation of glyph packing based on a Delaunay triangulation to speed up the clustering process and reduce costs for neighborhood searches. Our approach does not require a re--computation of acceleration structures when a plane is moved through a volume, which can be done interactively. We provide two methods for initial placement of glyphs to improve the convergence of our algorithm for glyphs larger and glyphs smaller than the data set's voxel size. \ The main contribution of this paper is a novel approach to glyph packing that supports simpler parameterization and can be used easily for highly efficient interactive \ data exploration, in contrast to previous methods.
- Mario Hlawitschka and Gerik Scheuermann and Alfred Anwander and Marc Tittgemeyer and Bernd Hamann. Tensor Lines in Tensor Fields of Arbitrary Order. In Proceedings of Third International Symposium on Visual Computing (ISVC 07), pages 341-350. Springer, 2007.
This paper presents a method to reduce time complexity of the computation of higher--order tensor lines. The method can be applied to higher--order tensors and the spherical harmonics representation, both widely used in medical imaging. It is based on a gradient descend technique and integrates well into fiber tracking algorithms. Furthermore, the method improves the angular resolution in contrast to discrete sampling methods which is especially important to tractography, since there, small errors accumulate fast and make the result unusable. Our implementation does not interpolate derived directions but works directly on the interpolated tensor information. The specific contribution of this paper is a fast algorithm for tracking lines tensor fields of arbitrary order that increases angular resolution compared to previous approaches.
- Mario Hlawitschka and Gerik Scheuermann. Detection of Neural Fibers using Higher Order Tensor Lines. 5th Leipzig Research Festival for Life Sciences 2006. Abstract veröffentlicht in: 5th Leipzig Research Festival for Life Sciences 2006. Herausgeber: J. Thiery, A. Beck-Sickinger, T. Arendt. ISBN: 3-9810760-1-X
- Mario Hlawitschka and Gerik Scheuermann. HOT-Lines - Tracking Lines in Higher Order Tensor Fields. IEEE Visualization 2005. IEEE Computer Society, Los Alamitos, CA, USA, October 2005. Ausgezeichnet mit dem 3. Preis des MedVis-Award "Karl-Heinz-Höne-Preis 2006".
Tensors occur in many areas of science and engineering. Especially, they are used to describe charge, mass and energy transport (i.e. electrical conductivity tensor, diffusion tensor, thermal conduction tensor resp.) If the locale transport pattern is complicated, usual second order tensor representation is not sufficient. \ So far, there are no appropriate visualization methods for this case. \ We point out similarities of symmetric higher order tensors and spherical harmonics. A spherical harmonic representation is used to improve tensor glyphs. \ This paper unites the definition of streamlines and tensor lines and generalizes tensor lines to those applications where second order tensors representations fail. \ \ The algorithm is tested on the tractography problem in diffusion tensor magnetic resonance imaging (DT-MRI) and improved for this special application.
- Mario Hlawitschka and Julia Ebling and Gerik Scheuermann. Convolution and Fourier Transform of Second Order Tensor Fields. IASTED VIIP 2004.
The goal of this paper is to transfer convolution, correlation and Fourier transform to second order tensor fields. Convolution of two tensor fields is defined using matrix multiplication. Convolution of a tensor field with a scalar mask can thus be described by multiplying the scalars with the real unit matrix. The Fourier transform of tensor fields defined in this paper corresponds to Fourier transform of each of the tensor components in the field. It is shown that for this convolution and Fourier transform, the well known con volution theorem holds and optimization in speed can be achieved by using Fast Fourier transform algorithms. Furthermore, pattern matching on tensor fields based on this convolution is described.