Life science special report 161215 IY opt

VIRTUAL ABDOMINAL AORTIC ANEURYSM LIFE SCIENCES
KNOWLEDGE-BASED VIRTUAL
PROCESS TO AID ABDOMINAL AORTIC
ANEURYSM DIAGNOSIS
DR. SIMONE BARTESAGHI, PROF. GIORGIO COLOMBO
Politecnico di Milano
INTRODUCTION
A range of diseases and a stressful
lifestyle increase the vulnerability of the
human body. Among such disorders,
Abdominal Aneurysms (Figure 1) are a
serious, often fatal, condition. These are
related to the blood circulatory system
at the level of the abdomen and affect
5% of men and 1% of women over the
age of 65. In particular, an Abdominal
Aortic Aneurysm (AAA) weakens the walls
of the blood vessels, and can cause a
rupture, which leads to the spilling of a
large amount of blood into the abdominal
cavity. In 80 to 90% of cases (prehospital
deaths included), such a rupture
is fatal, while elective repair should
be considered only for any AAA with a
maximal diameter of 5.5 cm (men) or 5.0
cm (women). The classical intervention on
a pathological AAA is based on geometric
characteristics and on the perception
of pain by the patient. A physician
performs the evaluation of a possible
surgery after carrying out analyses on
biomedical images of the abdomen
obtained through scanning systems.
But not always is surgery necessary,
whereas in some instances the problem
may be underestimated. It follows that
surgeons need an index (risk indicator
or risk score) to make a decision. Over
the past years, a lot of novel diagnostic
techniques have been developed,
including computational tools such as
Computational Fluid Dynamics (CFD),
embedded automation and web services.
These tools, which are quantitative,
objective, repeatable and ethically friendly
due to their virtual, non-patient-specifi c
nature, also have limited costs compared
to the above-mentioned techniques.
FIGURE 1: CT scan with AAA; volumetric
visualization. Colors are used only for
visualization
The use of computational tools applied
to the analysis of blood fl ow in human
arteries has grown tremendously over the
last years. Nowadays, such tools provide
detailed and trustworthy information
on hemodynamic quantities, enabling
researchers to investigate a large number
of problems which were almost impossible
to tackle with traditional engineering fl ow
measurement techniques. Furthermore,
thanks to new imaging techniques, it is
now possible to perform computations
based on realistic geometries and under
real blood fl ow conditions. As such, fully
three-dimensional CFD models have
signifi cantly improved. Today, CFD allows
studies related to local hemodynamics
in the biomechanical processes of the
vascular bed to be carried out quickly
and accurately, and enables experts to
test and validate clinical and surgical
procedures more effi ciently than ever
before. State-of-the-art tools have been
developed to perform patient-specifi c
CFD simulations. Using these techniques,
novel risk rupture indicators have been
developed by using imaging methods from
FIGURE 2: Embedded workfl ow
representation
traditional diagnostic tools, such as the
Time Average Wall Shear Stress (TAWSS)
and the Oscillatory Shear Index (OSI).
ANALYSIS
In this study, simulations were performed
in order to extract rules, strategies and
procedures to automate the workfl ow
process from pre-processing to postprocessing.
For the pre-processing phase,
a benchmark geometry was used. Figure2
shows the workfl ow implemented by using
knowledge-based Java scripts to automate
STAR-CCM+’s mesher and solver. The
Oscillatory Shear Index (OSI) was used as
the risk indicator. This index is used to
identify regions on the vessel wall which are
subjected to highly oscillating WSS values
during the cardiac cycle. OSI is defi ned as:
where s is the wall position and
T is the cardiac cycle period.
SPECIAL REPORT
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