Osteoconductive Lattice Microarchitecture for Optimized Bone Regeneration
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Authors
Ghayor, Chafik
Rüegg, Jasmine
Nicholls, Flora
Chen, Tse-Hsiang
Weber, Franz E.
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Publication date
06/2018
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01A - Journal article
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Parent work
3D Printing and Additive Manufacturing
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Volume
6
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Mary Ann Liebert
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Abstract
Selective laser melting (SLM) is one methodology to realize additive manufacturing and is mainly used to join metal powder in a layer-by-layer manner to produce a solid three-dimensional (3D) object. For bone tissue engineering purposes, scaffolds can readily be designed as 3D data model and realized with titanium known for its excellent osseointegration behavior. The microarchitecture, that is, design with submillimeter features, of additively manufactured scaffolds is in many cases a lattice structure. This study aimed to apply SLM that allows a high degree of microarchitectural freedom to generate lattice structures and to determine the optimal distance between rods and the optimal diameter of rods for osteoconduction (bone ingrowth into scaffolds) and bone regeneration. For the biological readout, diverse SLM-fabricated titanium implants were placed in the calvarium of rabbits and new bone formation and defect bridging were determined after 4 weeks of healing. The results from the middle section of the defects show that with a lattice microarchitecture, the optimal distance between titanium rods is around 0.8 mm and the optimal rod dimension is between 0.3 and 0.4 mm to optimize defect bridging and bone regeneration.
Keywords
selective laser melting, titanium, bone regeneration, bone repair, osteoconduction, grid architecture, lattice architecture, additive manufacturing
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2329-7670
2329-7662
2329-7662
Language
English
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Yes
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Published
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Peer review of the complete publication
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Citation
DE WILD, Michael, Chafik GHAYOR, Simon ZIMMERMANN, Jasmine RÜEGG, Flora NICHOLLS, Felix SCHULER, Tse-Hsiang CHEN und Franz E. WEBER, 2018. Osteoconductive Lattice Microarchitecture for Optimized Bone Regeneration. 3D Printing and Additive Manufacturing. Juni 2018. Bd. 6. DOI 10.1089/3dp.2017.0129. Verfügbar unter: http://hdl.handle.net/11654/26981