In the past decade, with the proliferation of technology and the increase of its capability, Computational Fluid Dynamics (CFD) has become a popular tool in several applications. This combined with our desire of increasing human life expectancy has lead to the combination of CFD and medicine. Lately, computation hemodynamics has become a powerful and important mechanism to study the cardiovascular system and its pathologies. Aneurysms, embolisms and atherosclerosis are, among different diseases affecting the cardiovascular system, the most studied.

Comparison of the images and reconstructed geometries after different pre-processing methods identifies a possible uncertainty range for this patient specific study that should be considered when discussing prognosis and diagnosis in a clinically relevant context. 

 A variety of methods have been proposed for medical image filtering and enhancement. These have been largely used in the context of both improving the visual quality and robustness to subsequent automated numerical procedures, as segmentation or feature detection. Ideally desired structures would be uniformly enhanced while suppressing noise.

Here, different synthetic images are considered as well as artefacts. A robust pipeline procedure is selected and applied to three medical images of different modalities. The final goal is not only to optimize the pre-processing methodology but also analyze the effects of uncertainty in clinically acquired medical imaging to variability in the reconstructed virtual geometry and CFD measures largely used, in the literature, as correlator to disease.