Virtual Stenting in Vascular Intervention

• Virtual stenting is the computational simulation of stent or stent-graft deployment within patient-specific vascular geometries to enhance preoperative planning and device design.
• It leverages advanced methods like finite-element analysis, active contour models, and image-driven deformation to accurately predict device mechanics and vessel interaction.
• The approach integrates hemodynamic analysis to quantify parameters such as wall shear stress and pressure drops, aiding precise risk assessment and outcome prediction.

Virtual stenting refers to the computational simulation, planning, and analysis of vascular stent or stent-graft deployment in patient-specific vessel geometries. Through advanced computational models, image-derived anatomic reconstructions, and often real-time or near-real-time workflows, virtual stenting aims to provide quantitative predictions of device apposition, expansion, loading, hemodynamics, or procedural feasibility. Virtual stenting now underpins a range of precision-medicine applications in endovascular, neurointerventional, and coronary domains, serving as a core tool for preoperative planning, intraoperative guidance, device design, and patient-specific outcome prediction.

1. Foundational Concepts and Objectives

Virtual stenting simulates the deployment and mechanical interaction of endovascular devices within reconstructed vascular geometries derived from in vivo imaging (CT, MRI, angiography, or OCT). The primary objectives are:

• To predict final device configuration, location, and wall apposition in patient-specific anatomy.
• To quantify clinically relevant metrics such as stent expansion, sealing-zone area, or presence of malapposition.
• To anticipate hemodynamic or biomechanical consequences of stent placement, including residual pressure drop, wall shear stress, and risk of restenosis or endoleak.
• To evaluate device design, robustness to anatomical variation, and mechanical durability (including fatigue).

Many workflows integrate anatomical modeling (segmentation, centerline extraction), mechanical simulation (finite-element or beam models), image-based deformation (e.g., centerline inflation), and, in advanced pipelines, machine learning–based real-time surrogates. Validated accuracy, computational speed, and the ability to customize or interactively evaluate procedural plans are distinguishing requirements (Pionteck et al., 2021, Bisighini et al., 2023, Kopanitsa et al., 9 Dec 2025, Egger et al., 2016).

2. Vascular Imaging Segmentation and Geometric Reconstruction

Accurate representation of the vasculature and associated pathologies (e.g., aneurysm, stenosis) is prerequisite. Core tasks include:

• Lumen and wall segmentation: Thresholding, region-growing, and graph-based minimal-closure algorithms are deployed for 3D vessel segmentation from contrast-enhanced CT angiography, with morphological cleanup and iterative tracking for challenging outer wall recovery. A centerline extraction step often utilizes fast-marching or Dijkstra/A*-based schemes on intensity-weighted graphs, ensuring submillimetric accuracy in tortuous anatomy (Egger et al., 2011, Egger et al., 2013, Egger et al., 2016).
• Surface and centerline processing: Vessel masks are converted to triangulated meshes and parameterized centerlines using spline interpolation, enabling consistent frame placement for stent modeling and mesh construction. Radii at each ring or cross-section are computed via ray-casting in the normal plane or Euclidean distance transforms (Egger et al., 2013, Egger et al., 2016).
• Multi-modality approaches: For small-caliber vessels, pipelines may fuse OCT (cross-sectional resolution of stent struts) with angiographic-derived 3D centerlines for robust stent geometry registration and visualization (Yang et al., 2018).

3. Mechanical Modeling of Stent Deployment

Mechanical deployment simulation encompasses detailed treatment of device–artery and device–balloon interactions. The key approaches include:

• Finite-element (FE) beam models: Corotational Euler–Bernoulli or Simo–Reissner beam elements are used to discretize stent wires or struts, capturing nonlinear large-deformation, bending, and, where relevant, elasto-plasticity (e.g., for metallic or polymeric stents). Contact mechanics (penalty, mortar, or Lagrange-multiplier methods) enforce interaction between stent and vessel wall (Pionteck et al., 2021, Baylous et al., 2023, Datz et al., 18 Jul 2024, Bisighini et al., 2023).
• Active contour (ACM) methods: Stent surface models treat the device as a deformable mesh evolving under internal elastic (stretching, bending) and external (balloon expansion, wall collision) energies. The resulting Euler–Lagrange equations are solved via implicit or explicit time-marching until mechanical equilibrium, producing physically plausible fit to vessel walls (Egger et al., 2016, Egger et al., 2013).
• Image-driven deformation: For rapid deployment representation in silico, image-based or centerline-based inflation approaches deform a prescribed interval of the vessel centerline and adjust radial geometry to achieve prescribed stent size, optionally constrained by bending stiffness parameters (Ma et al., 2022).
• Material characterization: Stent elements may be modeled as linear elastic (Phynox, 316L steel), superelastic shape-memory alloys (Nitinol, incorporating martensitic–austenitic phase behavior), or even as polymers with assigned surrogate elastic properties. Fatigue analysis leverages strain-based S–N curves (e.g., Pelton criteria for Nitinol), predicting element-level risk under cyclic loading in dynamic models (Baylous et al., 2023).

4. Virtual Stenting Pipeline Architectures

Workflow diversity is apparent across clinical scenarios and device types:

• Abdominal aortic aneurysms (AAA) and EVAR: Four-stage pipelines combine image-based stent ring detection (2D to 3D barycenter back-projection), axisymmetric geometric reconstruction, rotation minimization against intraoperative imaging, and detailed per-ring deployment via beam-contact models. Locked vs. free ring identification and rigid "deployment box" constraints facilitate high-fidelity pose and contact simulation below 3 mm clinical error thresholds (Pionteck et al., 2021).
• Coronary and cerebral stenting: Mixed-dimensional FE approaches couple 1D beam stent models with fully 3D hyperelastic arterial walls, allowing high-resolution contact analysis, realistic balloon expansion, and residual recoil studies. Deployment proceeds in pseudo-time-stepping under quasi-static loading, capturing principal wall stresses at critical locations (e.g., stent edges, calcified regions) (Datz et al., 18 Jul 2024).
• Bifurcated and Y-stent planning: ACM models facilitate automated extension from centerline-extracted trifurcated meshes to final fit with patient artery, with simultaneous measurement of diameters at multiple locations for device selection (Egger et al., 2016, Egger et al., 2013).
• Reduced order modeling (ROM) and machine learning surrogates: High-fidelity FE deployment snapshots are compressed via Proper Orthogonal Decomposition (POD), with Gaussian Process Regression (GPR) or classification models enabling real-time prediction of deployment outcome and anatomical fit purely from centerline or parametric descriptors. ROMs achieve average errors below CT/angiography resolution (<0.15 mm), providing instantaneous "what-if" queries for interventional planning (Bisighini et al., 2023).
• Intraoperative, real-time guidance: For FEVAR, pipelines integrate automated marker segmentation in fluoroscopy (Focal-U-Net), RPnP-based segment pose estimation, and fast mesh assembly, maintaining <0.2 s pipeline latency at clinically relevant frame rates. Adapted graph-convolutional networks (GCNs) enable partial-deployment stent shape instantiation when reference 3D marker positions are unknown (Zhou et al., 2017, Zheng et al., 2019).

5. Hemodynamic Analysis and Physiology-Aware Virtual PCI

Beyond geometric and mechanical endpoints, virtual stenting increasingly incorporates downstream physiologic and hemodynamic effects:

• Angiography-derived metrics: Integrated pipelines process cine-angiography via deep-learning detection (YOLOv8m), semantic segmentation (DeepLabV3+), centreline/diameter extraction, and per-mm relative flow capacity (RFC) calculation (diameter’s fourth power as Poiseuille surrogate). Virtual stent planning involves interactive editing of target diameter profiles, deterministic smoothing, and instantaneous recomputation of quantitative flow ratio (QFR) via a 1D lumped-parameter model (Kopanitsa et al., 9 Dec 2025).
• CFD-based studies: Lattice-Boltzmann (MRT-D3Q19) approaches evaluate flow patterns, wall shear stress, and pressure drop post-virtual stenting in aortic coarctation. Local radius and centerline errors in geometry deformation are maintained below 1 mm, with LES/DNS-based CFD solutions validated against 4D-Flow MRI (Ma et al., 2022).
• Patient-specific in-stent restenosis risk: Mixed-dimensional FE models provide spatially resolved wall stress mapping after angioplasty, with direct correlation between mechanical overloading at stent edges and clinically observed restenosis sites (Datz et al., 18 Jul 2024).

6. Validation, Limitations, and Clinical Integration

• Accuracy benchmarking: Quantitative comparisons to physical phantoms, 3D printed models, or in vivo imaging ground truth (CT, MRI, FFR) are standard. Submillimetric errors in stent location, diameter, or mesh surface are routinely reported and used as evidence of clinical translatability (Yang et al., 2018, Pionteck et al., 2021, 2310.5755, Kopanitsa et al., 9 Dec 2025).
• Timings and computational cost: Mechanical/deployment solvers may require ~10–15 minutes per patient for full FE simulation, with real-time (<0.2 s/frame) achievable in deep learning/ROM workflows. Parallelization and GPU-acceleration strategies are often proposed for next-generation developments (Pionteck et al., 2021, Bisighini et al., 2023, Zhou et al., 2017).
• Limitations: Commonly cited sources of error include rigid wall assumptions, omission of fabric/graft behavior, simplified or non-physiologic balloon models, segmentation inaccuracy, and lack of explicit modeling for plaque, thrombus, or tissue remodeling. Patient-specific fatigue life computation for TAVR depends on material model calibration and cycle-count extrapolation (Baylous et al., 2023, Datz et al., 18 Jul 2024).
• Clinical workflow integration: Embedding virtual stenting modules into imaging consoles or planning suites (with live overlays and interactive planning) is feasible via modular architecture and algorithmic acceleration, supporting iterative adjustment and what-if analysis in intraoperative settings (Kopanitsa et al., 9 Dec 2025, Pionteck et al., 2021, Egger et al., 2016).

7. Future Directions and Emerging Paradigms

Trends in virtual stenting research indicate ongoing transitions toward:

• Fully automated, end-to-end stenting and physiology pipelines combining computer vision, ML surrogates, and physics-based modeling, reducing human intervention and time-to-result (Kopanitsa et al., 9 Dec 2025).
• Biomechanically coupled simulations including growth, remodeling, and long-term clinical endpoint prediction, potentially leveraging dynamic or accelerated-integration solvers (Datz et al., 18 Jul 2024, Baylous et al., 2023).
• Enhanced mechanobiological insight, coupling predicted wall stresses and flow disturbances with risk models for restenosis and device failure, targeting personalized and preventive therapies (Ma et al., 2022, Datz et al., 18 Jul 2024).
• Extension to complex devices (bifurcated/branched grafts, fenestrated FEVAR devices, flow diverters, biodegradable stents) and heterogeneous vascular beds (coronary, neuro, peripheral).
• Integration with robotic navigation and intraoperative adaptation, capitalizing on rapid 3D instantiation frameworks for real-time procedural guidance (Zhou et al., 2017, Zheng et al., 2019).

Virtual stenting now constitutes a rigorous, clinically validated, and rapidly evolving paradigm for computational device planning, with broad implications for vascular intervention, device engineering, and the future of precision endovascular therapy.