Biomechanical Optimization of Lumbar Fusion Cages with a Porous Design: A Finite Element Analysis

Lumbar interbody fusion (LIF) is a standard treatment for spinal instability, yet postoperative subsidence and adjacent segment degeneration (ASD) remain critical challenges. This study evaluates the biomechanical efficacy of personalized porous fusion cages—featuring Gyroid (G-Cage) and Voronoi (V-...

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Bibliographic Details
Main Authors: Chenkai Zhu, Kan Deng, Zhenzong Shao, Yong Wang
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Applied Sciences
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Online Access:https://www.mdpi.com/2076-3417/15/10/5384
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Summary:Lumbar interbody fusion (LIF) is a standard treatment for spinal instability, yet postoperative subsidence and adjacent segment degeneration (ASD) remain critical challenges. This study evaluates the biomechanical efficacy of personalized porous fusion cages—featuring Gyroid (G-Cage) and Voronoi (V-Cage) architectures—against classic (C-Cage) and personalized (P-Cage) designs, aiming to enhance stability and mitigate subsidence risks. A finite element model of the L3–L4 segment, derived from CT scans of a healthy male volunteer, was developed to simulate six motion modes (compression, rotation, flexion, extension, and left/right bending). Biomechanical parameters, including range of motion (ROM), cage stress, endplate stress, and displacement, were analyzed. The results demonstrated that the V-Cage exhibited superior performance, reducing ROM by 51% in extension, cage stress by 41.7% in compression, and endplate stress by 63.7% in right bending compared to the C-Cage. The porous designs (G-Cage, V-Cage) exhibited biomimetic stress distribution and minimized micromotion, which was attributed to their trabecular-like architectures. These findings highlight the Voronoi-based porous cage as a biomechanically optimized solution, offering enhanced stability and reduced subsidence risk when compared to classic implants. The study underscores the potential of patient-specific porous designs in advancing LIF outcomes, warranting further clinical validation to translate computational insights into practical applications.
ISSN:2076-3417