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Published on: December 6, 2024
Patient-Specific Aortic Growth Prediction in Type B Aortic Dissection Using Finite-Element Simulation Based on the
Xue Liang1,2, Marc-Philipp H Schmid2, Minliang Liu3
1Carlyle Fraser Cardiothoracic Research Laboratory, Division of Cardiothoracic Surgery, Emory University School of Medicine, 1365 Clifton Road, Suite A 2213, Atlanta, GA, 30322, USA.
Purpose:
Type B aortic dissection (TBAD) managed with optimal medical therapy (OMT) carries a 40-50% risk of progressive aneurysmal degeneration. Predicting which patients will experience significant aortic enlargement is critical for timely intervention. This study aimed to develop and evaluate a patient-specific finite-element (FE) framework for predicting aortic geometry and diameter growth in uncomplicated TBAD managed with OMT.
Methods:
We extended our previously developed wall stress-driven growth framework and applied it to seven TBAD patients with each having three serial computed tomography (CT) scans. 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. Patient-specific aortic geometries from the three serial CT scans were obtained for each of the seven TBAD patients. Using the first two CT images and each patient's 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.
Results:
Predicted aortic geometries and dimensions matched well with in vivo measurements: for all seven patients, the absolute percent error was < 3.5% for the predicted maximum aortic diameter and < 4% for the predicted mean diameter across all locations.
Conclusion:
This proof-of-concept study demonstrates the feasibility of patient-specific TBAD growth forecasting from routine CT imaging and blood pressure, which could support individualized surveillance planning and earlier, evidence-based decision-making for patients managed with OMT.
