Providing suitable training for aspiring neurosurgeons is becoming more and more problematic. The increasing popularity of the endovascular treatment of intracranial aneurysms leads to a lack of simple surgical situations for clipping operations, leaving mainly the complex cases, which present even experienced surgeons with a challenge. To alleviate this situation, we have developed a training simulator with haptic interaction allowing trainees to practice virtual clipping surgeries on real patient-specific vessel geometries. By using specialized finite element (FEM) algorithms (fast finite element method, matrix condensation) combined with GPU acceleration, we can achieve the necessary frame rate for smooth real-time interaction with the detailed models needed for a realistic simulation of the vessel wall deformation caused by the clamping with surgical clips. Vessel wall geometries for typical training scenarios were obtained from 3D-reconstructed medical image data, while for the instruments (clipping forceps, various types of clips, suction tubes) we use models provided by manufacturer Aesculap AG. Collisions between vessel and instruments have to be continuously detected and transformed into corresponding boundary conditions and feedback forces, calculated using a contact plane method. After a training, the achieved result can be assessed based on various criteria, including a simulation of the residual blood flow into the aneurysm. Rigid models of the surgical access and surrounding brain tissue, plus coupling a real forceps to the haptic input device further increase the realism of the simulation.
We present a novel simulation system of blood flow through intracranial aneurysms including the interaction
between blood lumen and vessel tissue. It provides the means to estimate rupture risks by calculating the distribution
of pressure and shear stresses in the aneurysm, in order to support the planning of clinical interventions. So
far, this has only been possible with commercial simulation packages originally targeted at industrial applications,
whereas our implementation focuses on the intuitive integration into clinical workflow. Due to the time-critical
nature of the application, we exploit most efficient state-of-the-art numerical methods and technologies together
with high performance computing infrastructures (Austrian Grid). Our system builds a three-dimensional virtual
replica of the patient's cerebrovascular system from X-ray angiography, CT or MR images. The physician
can then select a region of interest which is automatically transformed into a tetrahedral mesh. The differential
equations for the blood flow and the wall elasticity are discretized via the finite element method (FEM), and the
resulting linear equation systems are handled by an algebraic multigrid (AMG) solver. The wall displacement
caused by the blood pressure is calculated using an iterative fluid-structure interaction (FSI) algorithm, and the
fluid mesh is deformed accordingly. First simulation results on measured patient geometries show good medical
relevance for diagnostic decision support.