Coronary artery disease (CAD) is one of the leading causes of mortality worldwide, and it is strongly associated with the local hemodynamics acting on the vascular wall. Abnormal wall shear stress (WSS) patterns have been linked to endothelial dysfunction, plaque initiation, and atherosclerotic plaque progression. Computational fluid dynamics (CFD) has emerged as a powerful tool for the non-invasive investigation of coronary hemodynamics in patient-specific vascular geometries. In this context the aim of this thesis was to develop a reproducible computational workflow for the reconstruction, preprocessing, meshing, and analysis of patient-specific local hemodynamics of coronary artery trees starting from coronary computed tomography angiography (CCTA) datasets.