Biomedical Engineering Reference
In-Depth Information
For example, the extraction of information from both the hard and the soft tissues,
acquired with different imaging modalities (CT and MRI), is essential in an anatom-
ical study. It can be used to obtain information in many musculoskeletal clinical
applications [
9
].
On the other hand, the
multiscalar requirement
, i.e. mixing information between
scales, is needed because systems and pathologies in the human body are often
hierarchical. Events on the cellular scale propagate upwards to the tissue or organ
levels (Fig.
5.3
). In some cases, a complete evaluation of medical risks can only be
obtained if data from different scales is available [
10
].
For example, musculoskeletal diseases depend on several factors from multiple
scales. For a complete study, information sources from different scales have to be
considered. Specifically, studies of cartilage [
11
] have shown the impact of extra-
cellular matrix (molecular) components on macroscale elements. The degradation
of their nanoscale structure greatly influences the behavior of the tissue. This causes
degeneration with age, injury, or diseases such as osteoarthritis. Sources of infor-
mation range from cross-sectional histology at the cellular level, to body motion
captures at the behavior scale, with additional data on the tissue and organ level
in between (Fig.
5.2
). Research projects as of [
12
-
14
] prove that the integration of
multiscale data can lead to deeper understanding with practical consequences.
Another example is the study of the cardiovascular system. In [
17
], it is shown
that the multiscale conception of the human blood circulation system, from mole-
cular to organ level, can enhance the understanding of diseases, such as vascular
atherogenesis.
Until today no major advances have been made in multiscale biomedical visual-
ization, except in the domains of genomics and proteomics [
18
]. Hence, many authors
called for efforts to create a multidisciplinary work in an integrated visualization [
19
,
20
] of biological data: “the revolution in biological data visualization hasn't started
yet” [
21
].
5.3 Visualization Helps Understanding Science
Scientific visualization
[
22
] presents numerous types of data that are inherently spatial
in a visual form. It typically aims to represent data based on physical measurements
e.g. obtained via acoustic waves (sonography) or often via electromagnetic waves
including e.g. digital X-ray - and CT-imaging as well as light microscopy. Electronic
microscopy uses electron beams to illuminate a specimen producing a magnified
image. Most of the aforementioned imaging techniques already use sophisticated
mathematical computations evaluating the respective physical measurements in an
initial processing step needed (to prepare further steps) for presenting an appro-
priate visualization of the respective spatial data. Those computations may include
methods from signal processing such as Hilbert transform for sonography imag-
ing or Radon transform for CT-imaging [
23
,
24
]. Further processing steps may use
more mathematical methods e.g. for segmentation of CT-volume data separating
