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.