Growth Prediction of Type B Aortic Dissections Using Wall-Stress-Driven Finite Element Simulation Based on the Unified-Fiber-Distribution (UFD) Model
Liang, X.; Schmid, M.-P.; Liu, M.; Cebull, H.; Zhang, M.; Xu, S.; Naeem, M.; Oshinski, J.; Elefteriades, J.; Gleason, R.; Leshnower, B.; Dong, H.
Show abstract
Type B aortic dissection (TBAD) is a serious, potentially life-threatening condition which occurs when a tear develops in the inner lining (intimal layer) of the descending aorta, causing the layers of the aortic wall to separate (dissect) and creating true and false lumens. TBAD can be classified into complicated and uncomplicated types based on the presence of complications (e.g., rupture or malperfusion). For complicated TBAD, the standard treatment is thoracic endovascular aortic repair (TEVAR) with a stent graft. Uncomplicated TBAD can be managed with optimal medical therapy (OMT). Predicting growth and aneurysmal progression of uncomplicated TBAD is clinically important for timing of intervention during OMT. In this study, we extended our previously developed finite element (FE)-based tissue growth framework and applied it to predict the precise geometry and diameter growth of TBAD. Specifically, the unified-fiber-distribution (UFD) model was applied to describe aortic wall mechanics, and a novel centerline-based algorithm was developed to determine the local material coordinates of aortic tissues. A linear kinematic growth law related to local wall stress was used for tissue growth. Patient-specific aortic geometries from three serial computed tomography (CT) scans were obtained for seven patients with TBAD. Using the first two CT images and each patients blood pressure, inverse FE analysis was performed to obtain patient-specific growth parameters. These parameters were then used to simulate forward growth and predict geometry at the third time point. Predicted aortic geometries and dimensions matched well with in vivo measurements: across all patients the maximum diameter error was below 3.5% and the mean diameter error below 4%. Such accurate patient-specific growth forecasts demonstrate the potential of this computational framework to support clinical decision-making in uncomplicated TBAD.
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