Transactions on Additive Manufacturing Meets Medicine
Vol. 6 No. S1 (2024): Trans. AMMM Supplement
https://doi.org/10.18416/AMMM.2024.24091791
Additive manufacturing of personalized endovascular NiTi implants for intercranial aneurysms
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Copyright (c) 2024 Sandra Herzig; Christian Rotsch, Michael Werner, Patrick Ackert, Volker Trösken, Tim Lenz-Habijan, Felix Borges, Alexander Pugachev, Sebastian Wittor, Matthias Gawlitza
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Abstract
The shape memory alloy Nickel-Titanium (NiTi) is a common material used in the field of medical engineering due to its mechanical and functional properties and its biocompatibility. In addition to conventional manufacturing processes, NiTi can be processed with laser powder bed fusion (LPBF) and adapted scan strategies, rather than conventional strategies, to achieve small feature sizes, and more homogeneous and finer structures. One common application of NiTi is stents, which are typically manufactured either by braiding or laser cutting techniques. It should be noted that both methods are not suitable for the production of individualised implants. However, in the case of complex aneurysms, the production of individualised solutions is required. A common treatment for complex aneurysms is currently an off-label use of standard implants, but this still requires a significant degree of experience of the attending physician.
This work will demonstrate a novel workflow for the generation of patient-specific NiTi implants, derived from angiography results with a particular focus on intracranial aneurysms. The workflow employs an automated segmentation of the aneurysms and linked blood vessels based on the angiography scan to simulate the behaviour of the aneurysm and various possible implant geometries. Following the selection of the most suitable geometry, the implant will be manufactured via LPBF. Subsequently, a series of post-processing steps are undertaken to ensure that the requisite properties are met. Concurrently, an appropriate feeding system is developed and incorporated into the simulation. This presentation will demonstrate preliminary results along the workflow chain, as this work is still ongoing.