The clinical stakes are considerable. During stent-assisted treatment, a catheter is inserted through the femoral artery in the groin or the radial artery in the wrist and navigated through tortuous, branching vessels deep inside the brain. The stent must then be positioned so that its ends rest in straight sections of the vessel, providing stable anchoring while fully covering the aneurysm orifice. Misjudging the geometry can lead to stent migration, arterial occlusion, vessel damage, or stroke. Stent selection itself is a delicate balancing act: the device must match the diameter and length of the parent artery, accommodate the curvature of the vascular path, and leave adequate landing zones on both sides of the aneurysm neck. Traditional planning based on CT angiography, MR angiography, or digital subtraction angiography flattens this complex three-dimensional puzzle onto two dimensions, forcing clinicians to reconstruct the anatomy mentally at the very moment when errors matter most.

The Krakow team, led by Wojciech Chmiel together with Piotr Szwed, Joanna Kwiecień, Michał Turek and colleagues, built their platform around a workflow engine that automates the entire chain of image processing. Starting from volumetric medical images in NIfTI format, the system performs vessel segmentation using threshold-based binarization techniques such as Otsu’s method, cleans the resulting point clouds with the DBSCAN clustering algorithm to strip away outliers, and then computes the vessel’s medial axis, the invisible central spine along which a stent naturally wants to sit. The centerline extraction is particularly elegant: a distance-based potential field is evaluated for every interior point of the vessel, with the lowest potential values forming a valley along the vessel’s core, and the Dijkstra shortest-path algorithm then traces the optimal route between two operator-selected endpoints. Because the raw path follows a voxel grid and emerges as a staircase curve, a Savitzky-Golay filter smooths it into a anatomically plausible centerline without erasing genuine high-frequency curvature.

Once the centerline is established, the system computes a radial profile at each point, casting 120 rays spaced three degrees apart in a plane orthogonal to the axis and measuring the distance to the vessel wall along each one. These profiles act as a geometric fingerprint of the artery, allowing the software to pinpoint aneurysm necks and bifurcations with remarkable fidelity. At a spatial resolution of 0.05 millimeters per voxel, the smoothed profiles capture subtle variations in lumen diameter that would be invisible on a standard angiographic display. The operator then marks an attraction region on the centerline, a deliberately generous zone within which the stent simulation will unfold, and the processed mesh is exported to a format compatible with Microsoft’s HoloLens headset, complete with correctly oriented surface normal vectors that are essential for realistic shading and depth perception in stereoscopic rendering.

The heart of the system is its stent expansion simulation, which the authors describe as a computationally efficient, slice-based geometric approximation rather than a full biomechanical model. The stent is represented as a sequence of planar cross-sectional slices connected into a tubular triangular mesh. Each slice is iteratively expanded using cylindrical transformations, and when the expanding ring encounters the vessel wall, contact points freeze in place while the remaining points continue to move outward, deforming the ring to conform to the arterial geometry. These contact points are called support points, and the final slice border is interpolated through them using cubic splines. The researchers implemented four interaction modes, from rigid slice with rigid vessel to fully flexible combinations, and chose the flexible slice, rigid vessel configuration as the default because cerebral arteries do not dilate significantly during stent deployment. Restoring forces between neighboring mesh nodes smooth the vessel surface and prevent expansion artifacts, and stopping criteria based on slice asymmetry and area stagnation freeze slices that can no longer expand meaningfully.

What makes this approach genuinely practical for the operating room is speed. Full finite element simulations of stent deployment, while physically rigorous, are far too slow for real-time interaction. The Krakow team’s geometric approximation produces plausible, collision-aware stent configurations in seconds, generating what they call a solution space: a discrete set of candidate stent positions from which the clinician can select the configuration that best matches a specific device’s nominal diameter and foreshortening behavior. The system can even test microcatheter navigability, using rigid slice expansion to determine whether catheters with outer diameters between roughly 0.5 and 0.93 millimeters can physically pass through a given vascular segment. If slices freeze along the path, that signals a passage too tight for the chosen device, an early warning that could redirect the entire treatment strategy before the patient is ever on the table.

The augmented reality layer is where the system becomes genuinely striking. Through the HoloLens application, the operator sees a magnified, stereoscopic rendering of the patient’s vessel floating in the operating room, with semi-transparent stent models that can be picked from a virtual panel, moved into the artery, rotated, and snapped into alignment along the centerline, all through hand gestures detected by the headset’s cameras. Because touching a keyboard or mouse in a sterile surgical environment is impractical, the gesture subsystem was built on the Mixed Reality Toolkit 2 API and works even while the operator wears rubber surgical gloves. Perhaps most remarkably, the vessel model is scaled and calibrated so that the clinician can literally walk inside the rendered artery, inspecting the stent surface and its apposition to the vessel wall from the inside, an perspective no flat screen can provide.

Meeting the HoloLens’s demanding performance constraints required serious engineering discipline. The headset refreshes at 60 to 72 frames per second, leaving a render budget of under 16.6 milliseconds per frame to avoid inducing motion sickness, and stereoscopic rendering doubles the workload by requiring separate viewports for each eye. The team kept active geometry below 150,000 polygons per frame, minimized draw calls through batching, employed mobile-optimized shaders, and used Single-Pass Instanced Rendering to process both eye viewports in a single GPU execution. Late Stage Reprojection dynamically adjusts each frame’s focal point just before display, compensating for head-tracking latency and preventing parallax errors between the virtual models and the physical environment. These optimizations ensure that the immersive experience remains stable and comfortable even during prolonged planning sessions.

Beyond its intraoperative role, the platform doubles as a training and education tool. Every action, parameter choice, and correction made by a specialist during a workflow is recorded in a procedure repository, creating an audit trail that can be replayed for educational purposes or, subject to appropriate governance, used to train future artificial intelligence algorithms. This human-in-the-loop feedback cycle connects clinical expertise directly to algorithm development, addressing a well-known weakness in medical AI: the scarcity of large, expertly annotated datasets. The system’s workflow architecture is deliberately modular, meaning that today’s reference implementations of segmentation and simulation can be swapped for more advanced methods, including deep-learning approaches, without rebuilding the user interface or retraining clinicians on a new platform.

The authors are candid about limitations. The geometric stent simulation has not yet been quantitatively validated against benchtop deployment experiments, manufacturer data, or full finite element models, and it should not be regarded as more physically accurate than rigorous biomechanical approaches. Its advantages lie elsewhere: interactive speed, direct integration with the augmented reality workflow, and the ability to explore many candidate configurations in the time a single FEM run would take. Augmented reality headsets also impose inherent constraints on field of view and graphical resolution, though the team argues that vision-based tracking and semi-transparent rendering preserve sufficient localization precision even at high magnification. Looking forward, the researchers envision AI algorithms automating critical stages of image processing and generating comprehensive libraries of interventional scenarios. For now, the system stands as a compelling demonstration that 3D imaging, fast simulation, and immersive visualization can converge into a single tool that may make one of neurosurgery’s most delicate procedures measurably safer, more precise, and easier to teach.

Subject of Research: Augmented reality and 3D imaging support for stent-assisted treatment of cerebral aneurysms

Article Title: A Visual Support System for Stent-Assisted Treatment of Cerebral Aneurysms Using 3D Imaging and Augmented Reality

Article References: Chmiel, W., Szwed, P., Kwiecień, J., Turek, M., Jȩdrusik, S., Pałka, D., Waśniewski, M., Kadłuczka, P., Gumiela, D., & Zeja, A. (2026). A Visual Support System for Stent-Assisted Treatment of Cerebral Aneurysms Using 3D Imaging and Augmented Reality. Neuroinformatics, 24(4), Article 65. https://doi.org/10.1007/s12021-026-09812-2

Image Credits: AI Generated

DOI: 10.1007/s12021-026-09812-2

Keywords: cerebral aneurysm, augmented reality, stent-assisted coiling, 3D imaging, medical simulation, HoloLens, neurointerventional surgery, vessel segmentation, medial axis, workflow engine, digital health, Neuroinformatics