In our quickly aging world today, the worldwide population is experiencing an increased risk of Alzheimer’s Disease, a neurodegenerative disease known to have a higher risk with age. With alleviation of symptoms being the best that can be done for patients suffering from Alzheimer’s Disease, there is an imperative need for early diagnosis of the disease for early intervention efforts to reduce mortality rates. As such, there is a great interest in better understanding the dynamics of cerebrospinal fluid (CSF) and its exchange with the vasculature. Currently, noninvasive in-vivo human methods are limited; therefore, most existing results originate from animal models. One promising approach is probing the kinetics of dynamic contrast using PET imaging and different modeling methods. In addition to the traditional compartmental modeling method, in this study, we develop a CFD model that resolves spatial transport of the radiopharmaceutical contrast agent used in PET imaging. This study provides a comparative analysis of these two modeling approaches. To establish the efficacy of each method, we solved an inverse problem by identifying the optimal turnover rates that minimized the difference between each model prediction and clinical measurements. The result of this process indicates that the compartmental model is more aligned with clinical measurements than the CFD model (average compartmental model error 6.9% versus 12% for CFD). This higher CFD error is likely reflective of the inaccurate boundary conditions adopted in this study that are difficult to extract from clinical measurements. There was no significant correlation found between turnover time and studied parameters: age, ventricle volume, body mass index (BMI), amyloid-β and tau accumulation, and Apolipoprotein E (APOE) genotype. Despite its higher overall error, the CFD modeling approach presents a unique advantage as it is the only method that is able to capture the dependence of turnover time on the simulated brain compartment volume.