The purpose of vascular reconstruction is to redirect and augment blood flow, or perhaps repair a weakened or aneurysmal vessel. Many times the optimal procedure is obvious, but this is not always the case, for example, in a patient with complicated or multi-level disease. Pre-operative surgical planning will allow evaluation of different procedures a priori, under various physiologic states such as rest and exercise, thereby increasing the chances of a positive outcome for the patient.

The surgical plannning software demonstration contained in this site was developed at the Stanford University Cardiovascular Biomechanics Research Laboratory under the guidance of Prof. Charles Taylor, Dr. Christopher Zarins, and Prof. Thomas J.R. Hughes. This software was first demonstrated at the SVS Critical Issues Forum in San Diego, CA on June 8, 1998.

The specific case is of a patient with an occluded right iliac artery, a partially occluded left iliac artery, an occluded left profunda, and a diffusely diseased right SFA. Images and vascular lab data is included in the clinical history section of the demo.

The vascular geometry was extracted from an MR data set which was acquired with gadolinium contrast. This geometry was discretized, and the flow solutions under rest and exercise were solved using the finite element method. Since these solutions have been precomputed for the purpose of this demonstration, there are four procedures which can be compared: AFB w/ ES prox anast, AFB w/ EE prox anast, left iliac angioplasty w/ FF bypass, and left iliac angioplasty w/ FF and FPop bypasses. Detailed flow results are included in the treatment planning section of the demo.

This demonstration is an illustration of new tools and technology that will be available in the future to help us in our surgical and interventional treatment of patients with vascular disease. A word of caution: these are simulations and calculations. This is a demonstration of the feasibility of surgical planning. Actual flow calculations and solutions must be thoroughly validated before they can be used to predict clinical outcomes.