Medical Imaging on the ReaCTor: an E-Science Demonstrator
A. Steed, D. Alexander, P. Cook, C. Parker
A collaboration between the EQUATOR IRC and the MIAS IRC
Visualisation of 3D medical image volumes of the human brain is an
important issue for diagnosis of pathologies and pre-surgical planning
as well as basic understanding of anatomy. The development of
effective visualisation tools is hard enough for standard imaging
data, such as MR and PET measurements, but has become critical with
the introduction of new modalities such as diffusion, perfusion,
functional and permeability imaging, in which considerably more
information is gathered. In diffusion imaging, for example, the
measurement acquired at each voxel in the image is a complex shape
describing the local connectivity of tissue, which is particularly
useful in the brain, [Pierpaoli, et al, Diffuse Tensor Imaging of the
Human Brain, Radiology, 1996]. At UCL-CS, we have developed
interactive immersive visualisation tools for this type of data. One
example view from this system can be seen in Figure 1.
Figure 1: User re-configuring the visualisation within the ReaCTor
The demonstration uses the UCL ReaCTor, an Immersive Projection
Technology (IPT) system similar to a CAVE(tm). The ReaCTor is powered
by an SGI Onyx2 with four Infinite Reality2 pipes and 8
processors. The ReaCTor is a four-sided device, with three walls and a
floor.
The 3D voxel data is presented in head-tracked stereo in real-time and
the immersed user is provided with a variety of configuration and
exploration tools. The user can collaborate with a colleague on
another facility, either another IPT or a desktop system. We have
built and tested clients for SGI and Microsoft Windows and have run
network trials within UCL and between the UCL ReaCTor and the
University of Reading ReaCTor.
Figure 2-4: Desktop Version
The example data set shown in Figure 1 is a diffusion-weighted MRI
scan comprising a 128x128x42 voxel space with each voxel representing
1.7x1.7x2.5mm. The visualisation comprises three main parts: textured
cut-planes showing the raw data set; arrays of polyhedra, coloured by
the degree of anisotropy and directed by the principle component of
the diffusion tensor; and 3D tracks extracted by tracking neuronal
fibre pathways through the voxel space [Conturo, et al, Tracking
Neuronal Fiber Pathways in the Living Human Brain,
Proc. Natl. Acad. Sci. USA, Vol 96, pp 10422-10427].
Tracks are computed using either matrix or eigenvector interpolation
of the data set [Kindlmann, et al, Strategies for Direct Volume
Rendering of Diffusion Tensor Fields, IEEE Transactions on
Visualization and Computer Graphics, 6(2)]. This is done remotely on a
Beowulf cluster. We are currently using 72 nodes of a 256-node cluster
within the department. The users can interactively select 3D regions
from which seed points are extracted and sent to the Beowulf for path
extraction. This is parallel process that can start to return results
within a few hundred ms. A selection region might result in 3-4000
tracks which would take about five seconds to compute in total.
This is an early stage demonstrator, but it is already providing a
platform for novel medical imaging work. For example, this is the
first demonstration, that we know of, that can compute connectivity
between two regions using tractography.
We have started evaluating the visualisation itself with colleagues
from neuro-science. Under the Equator project we will also do studies
of how users actually collaborate in the shared space, following up
previous work on the asymmetries in IPT to desktop interaction [Steed,
et al., Solving a 3D Cube Puzzle in a Collaborative Virtual
Environment: As Good as Really Being There Together?, Technical
Sketch, ACM SIGGRAPH 2001].
Figure 5-8: The Authors
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