From Individual to Population: Challenges in Medical Visualization - Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization - page 254

23.3 Challenges in Medical Visualization
23.3.1 Advances in Data Acquisition
Toshiba's 320-slice CT scanner, the Aquilion One, was introduced in 2007. It is able
to acquire five 320 slice volumes per second [ 31 ] and can thus image a beating heart.
Rapid and continuous advances in the dynamic nature and sheer magnitude of data
in this and other mainstream medical imaging necessitates improvements to existing
techniques in terms of computational and perceptual scalability.
High Angular Resolution Diffusion Imaging (HARDI) [ 70 ] and Diffusion Spec-
trum Imaging (DSI) [ 25 ] datasets contain hundreds of diffusion-weighted volumes
describing the diffusion of water molecules and hence indirectly the orientation of
directed structures such as neural fiber bundles or muscle fibers. This is a rather
extreme example of multi-field medical data that is becoming more relevant in both
medical research and clinical application. Completely new visual metaphors are
required to cope with the highly multi-variate and three-dimensional data of dif-
fusion weighted imaging in particular and many other new imaging modalities in
general.
Molecular imaging enables the in vivo imaging of biochemical processes at the
macroscopic level, meaning that, for example, pathological processes can be stud-
ied and followed over time in the same subject long before large-scale anatom-
ical changes occur. Examples are bioluminescence (BLI) and fluorescence (FLI)
imaging, two molecular imaging modalities that enable the in vivo imaging of gene
expression. Molecular imaging yields datasets that vary greatly in scale, sensitivity,
spatial-temporal embedding and in the phenomena that can be imaged. Each per-
mutation brings with it new domain-specific questions and visualization challenges.
Up to now, most of the visualization research has been focused on small animal
imaging [ 41 , 42 ], but due to its great diagnostic potential, molecular imaging will
see increased application in humans.
The integration of microscopy imaging is an essential task for the future, where
data handling, interaction facilities but also more classical rendering tasks such as
transfer function design become essential. With more and more large scale and 3D
microscopy data available, there aremany opportunities for visualization researchers.
Recent examples include techniques for interactively visualizing large-scale biomed-
ical image stacks demonstrated on datasets of up to 160 gigapixels [ 34 ] and tools
for the interactive segmentation and visualization of large-scale 3D neuroscience
datasets, demonstrated on a 43 GB electron microscopy volume dataset of the hip-
pocampus [ 33 ].
With these examples, we hope to have illustrated that advances in image acqui-
sition are continuous, and due to the increasing demands of modern society are
accelerating. Each new advance in imaging brings potentially greater magnitudes
and exotic new types of data, leading to new challenges for medical visualization.