Peripheral arterial disease (PAD), an obstruction of blood flow in the arteries in the extremity, most commonly involves the superficial femoral artery (SFA) and affects approximately eight million Americans . For patients with PAD, endovascular stenting can be a promising therapy that alleviates plaque obstruction and restores blood flow. However, stent fracture is a serious problem and it can lead to vascular complications such as restenosis, stent occlusion, and re-interventional procedures. Stent fracture is influenced by type of stent and local mechanical forces that are influenced by arterial wall composition including degree of calcification. Selection of stents for patients should take into account vascular complexities such as realistic in vivo arterial compliances and disease severity. The long term goal of this study is to establish through experimentation, modeling, and simulation analysis of stent fatigue in the diseased state. Previous studies have modeled arteries as homogenous conduits, and do not consider individual patient environments. There are no existing models for the prediction of stent fatigue that combine biomedical imaging data of the diseased superficial femoral artery, which is essential for realistic in vivo arterial compliances. In this study, an automated artery computer model based on ultrasound virtual histology will incorporate the histological disease condition of the arteries. Combined with stent modeling, this computer model is targeted to be streamlined for the clinical arena and used as a physician tool for virtual evaluation and stent selection in a patient arterial setting. This reserch proposes to generate a computer model tool to aid in stent selection of for patients with peripheral artery disease and validate it. It will involve expanding our current stent artery computer model to include plaques of varying compositions. The study encompasses development of a large patient artery model database, development of a large stent model database for commercially available stents, and integration of the stent and artery models to evaluate varying plaque compositions and their impact on stent fatigue. Each modeling step will be validated with a unique experimental setup under application of in vivo pressures. This work will lead to knowledge about arterial stiffness, stent-artery interfaces, boundary conditions, cardiac pressure interactions, restenosis, and stent fracture. Future work will be to implement the computer modeling tool clinically.
Computational models provide the foundation to examine stent fatigue life in pathological diseased environments. Development of an automated superficial femoral artery computer model based on ultrasound virtual histology will incorporate specific plaque morphologies from human data. The stent performance in arteries with varying plaque type and composition will identify disease features that contribute to stent fracture. The model will be validated with a unique experimental system under application of in vivo pressures. Once validated, this model can be utilized as a clinical tool for physicians to select the optimal stent for a patient.