Fast virtual and learning methods for minimum invasive treatment of Intracranial Aneurysm
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The rupture of Intracranial Aneurysms is the most severe form of stroke with high rates of mortality and disability. One of its primary treatments is to use stent or Flow Diverter to divert the blood flow away from the IA in a minimal invasive manner. Another treatment is to pack the aneurysm with elastic metal wires or Coils to block the aneurysm and help the aneurysm to occlude. Also, the combination of these two treatments is also used by clinicians. To optimize such treatments, it is desirable to provide an automatic tool for virtual stenting and virtual coiling before its actual implantation. In this dissertation, we propose a novel method, called ball-sweeping, for rapid virtual stenting. Our method sweeps a maximum inscribed sphere through the aneurysmal region of the vessel and directly generates a stent surface touching the vessel wall without needing to iteratively grow a deformable stent surface. Our resulting stent mesh has guaranteed smoothness and variable pore density to achieve an enhanced occlusion performance. Also, we propose a virtual coiling method, called Adaptive Energy Minimization (AEM) method. In this method, each coil is considered as an elastic wire. For the deployment of each coil, we first virtually deploy the wire inside the aneurysm at the desire starting point and direction. Then the coil is bended such that the entire coil is inside the aneurysm. At last the bending energy is minimized. We run this procedure several times to make sure we end up with a smooth coil that inside the aneurysm. Comparing to existing methods, our technique is computationally much more efficient with acceptable deployment.