## 1 Introduction  
Knowledge of the hemodynamic behaviors of blood flow in the vascular system is important in order to understand and treat vascular pathology [1; 2; 3]. While cardiovascular risk factors may decrease arterial distensibility and increase vessel wall thickness of the carotid artery [4], the development and progression of atherosclerosis in the carotid bifurcation is related to its local hemodynamic conditions, such as deceleration of blood flow, secondary flow, and shear stress--as has been shown in vitro, in animal models, and in magnetic resonance imaging (MRI) studies in vivo [5; 6; 7].  
New medical imaging techniques that measure the flow in the circulation system can demonstrate the pathophysiology of atherosclerotic vascular disease [8]. Computational fluid dynamics (CFD) is an important method of visualizing blood flow and is widely used to evaluate the cardiovascular system's local hemodynamic condition [9]. Recently, four-dimensional (4D) flow MRI has become a powerful tool for assessing blood flow. It is a feasible method of measuring the 3D distribution of the blood velocity vector field in vivo, allowing 3D flow structures to be examined with a single scan [10; 11]. 4D flow MRI has been used to obtain data about the blood-flow characteristics of the cerebral vascular system, the aorta, as well as the carotid arteries, and can highlight local and global hemodynamic conditions [5; 8; 9; 12; 13; 14].  
The ability to effectively simulate the flow and pressure of the carotid artery is essential for researchers to model the performance of medical devices as well as predict the evolution of cardiovascular disease. Velocity measurements can be useful for quantification of different flow metrics through 4D flow MRI. But the spatial and temporal resolution of this technique is limited due to restricted scan-time in the clinical scenarios [15; 16]. On the other hand, CFD is a computer simulation based on numerous assumptions requiring information about vascular morphologies, initial conditions, and boundary conditions. Previous studies showed that CFD can provide an accurate 3D velocity vector fields with high spatial and temporal resolution [17; 18]. However, this method is rarely applied clinically due to the existing cumbersome process of model reconstruction and calculation.  
There have been studies compared the measurement of 4D flow MRI with CFD in vivo. [PERSON] et al. [16] presented a method for quantifying wall shear stress and wall thickness at the human carotid bifurcation using a combination of MRI and CFD modeling and demonstrated this approach makes it ideal for carrying out future prospective studies of hemodynamics and plaque development or progression. Other reported similar findings that both 4D flow MRI and CFD have the potential to be used in the clinic and could provide vital information on disease progression [15]. Another study also compared the hemodynamics of intracranial aneurysm using 4D flow MRI and MR-based computational fluid dynamics and indicated a fair correlation between the two techniques [18]. However, one of the most challenging aspect of 4D flow MRI and CFD studies is having a large sample size due to existing technical limitations and a long time needed for the performances. On the other hand, difficulties in choosing appropriate boundary conditions have limited the application of this field in clinical practice. Hence, the aim of our study was to compare the results obtained through 4D flow MRI and image-based CFD techniques of the velocity and the flow pattern of 12 normal carotid artery bifurcations. With a larger sample size, we expect to provide more significant results. In this study, we generated 3D subject-specific models by employing flow results from 4D flow MRI for individual flow boundary conditions.