From medical images to the computational haemodynamics: An
efficient Pipeline for Image-Based Patient-Specific Analysis
Aneurysm, embolisms and atherosclerosis are, among different
diseases affecting the cardiovascular system, the most studied.
These pathologies include a variety of disorders and conditions
that affect the heart and the blood and are usually associated with
factors like biochemistry, haemodynamics and genetic
predisposition. These factors are specific to each individual and
it is important to represent accurately patient-specific
information to evaluate correctly clinical state either at
diagnosis and prognosis stages. Taking an example of a
configuration of the Aorto-Iliac bifurcation, we examine the
effects of image filtering and contrast enhancement on the
computational reconstructed geometry. Methods to quantify the
differences resulting in the images from the different filtering
methods are based on the Signal Noise Ration, pixel intensity
variance. Finally all the methods are applied to a synthetic image
to assure the most accurate sequence of images. Comparison of the
images and reconstructed geometries after different pre-processing
methods identify a possible uncertainty range for this patient
specific study that should be considered when discussing prognosis
and diagnosis in a clinically relevant context, mainly when
studying the measures of wall shear stress, wall shear stress
gradient, and oscillatory shear index which have been largely used
in the literature to correlate to disease. In this study we focus
on the effects of uncertainty in clinically acquired medical
imaging to variability in the reconstructed vessel geometry.
