DiffStent: Surrogate Adjoint Differentiation for Stent Deployment in Anisotropic Arteries

We present DiffStent, a differentiable finite element framework for simulating arterial deformation during in silico stent deployment, where nearly incompressible, anisotropic vessel walls undergo large deformation and frictional contact with the stent. DiffStent combines the fiber-reinforced Holzapfel–Gasser–Ogden constitutive model with an implicit discrete adjoint that differentiates the converged mechanical equilibrium without unrolling the nonlinear solver, thus reducing the memory cost by three orders of magnitude. We identify three mechanisms that can lead to inaccurate, unstable, or uninformative gradients: nonsmooth switching, state mismatch, and gradient cancellation. To address these, we apply a contact surrogate in the backward pass while retaining the original physical model in the forward pass. We show superior gradient accuracy of our method over AutoDiff and finite differencing across constitutive benchmarks, idealized stented vessels, and patient-specific calcified coronary anatomies, and use the resulting sensitivities to quantify how tissue properties and calcification patterns govern lumen expansion and arterial wall stress. Finally, we demonstrate that DiffStent enables gradient-based system identification from retrospective clinical images of atherosclerotic coronary anatomy. Together, these results establish a practical approach for differentiating through nonlinear biomechanical simulations with stiff constitutive laws and nonsmooth contact, enabling patient-specific parameter inference and optimization in computational cardiovascular mechanics.