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Cardiovascular disease (CVD) is the leading cause of death in the world. Considerable research has been done linking various risk factors to plaque progression and rupture which often lead to drastic clinical events such as heart attack and stroke.  However, methods transforming research results to clinical implementation are limited. There has been evidence indicated that mechanical stress and strain may be linked to plaque progression.  However, 3D plaque model construction is extremely time consuming making is near impossible for clinical implementations. 2D structure-only model is easy to make, but its stress/strain predictions are not good enough to serve as approximation to 3D solutions.  In this study, an in vivo IVUS based 3D Thin-Wall model was developed to approximate 3D FSI model for clinical implementations. Results from one patient  data (100 TW models) indicated that mean value of  maximum plaque wall stress (MPWS) and average plaque wall stress (APWS) from TW model were 1.9% and 3.0% higher than those from FSI model (MPWS: 127.2±74.3 kPa vs. 124.8±65.7 kPa , APWS: 65.9±18.8 kPa vs. 64.0±21.4 kPa). Mean value of maximum plaque wall strain (MPWSn) and average plaque wall strain (APWSn) from TW model were 1.3% and 1.6% lower than those from FSI model (MPWSn: 0.0760±0.0221 vs. 0.0770±0.0161, APWSn: 0.0572±0.0056 vs. 0.0581±0.0070). Wall thickness (WT) from TW model was 1.7% lower than those from FSI model (0.0630±0.0126 cm vs. 0.0640±0.0126).  At baseline, PWS and plaque progression measured by wall thickness increase (WTI) had no significant correlations using either FSI or TW models (FSI: r = 0.0446, p = 0.7684; TW: r = -0.0414, p = 0.7848). WT had significant negative correlation with WTI (FSI: r = -0.3337, p = 0.0234; TW: r = -0.3559, p = 0.0152). PWSn had weak positive correlation with WTI by FSI model (r = 0.2830, p = 0.0506), while this cannot be predicted by TW model (r = -0.1493, p = 0.3219). Large scale studies are needed to further investigate the feasibility of using TW models as approximation for FSI models in potential clinical implementations.