Spinal Motion Segments — I: Concept for a Subject-specific Analogue Model

Franceskides, Constantinos; Arnold, Emily; Horsfall, Ian; Tozzi, Gianluca; Gibson, Michael C.; Zioupos, Peter

Abstract

Most commercial spine analogues are not intended for biomechanical testing, and those developed for this purpose are expensive and yet still fail to replicate the mechanical performance of biological specimens. Patient-specific analogues that address these limitations and avoid the ethical restrictions surrounding the use of human cadavers are therefore required. We present a method for the production and characterisation of biofidelic, patient-specific, Spine Motion Segment (SMS = 2 vertebrae and the disk in between) analogues that allow for the biological variability encountered when dealing with real patients. Porcine spine segments (L1–L4) were scanned by computed tomography, and 3D models were printed in acrylonitrile butadiene styrene (ABS). Four biological specimens and four ABS motion segments were tested, three of which were further segmented into two Vertebral Bodies (VBs) with their intervertebral disc (IVD). All segments were loaded axially at 0.6 mm·min−1 (strain-rate range 6×10−4 s−1–10×10−4 s−1). The artificial VBs behaved like biological segments within the elastic region, but the best two-part artificial IVD were ∼15% less stiff than the biological IVDs. High-speed images recorded during compressive loading allowed full-field strains to be produced. During compression of the spine motion segments, IVDs experienced higher strains than VBs as expected. Our method allows the rapid, inexpensive and reliable production of patient-specific 3D-printed analogues, which morphologically resemble the real ones, and whose mechanical behaviour is comparable to real biological spine motion segments and this is their biggest asset.

Citation

Franceskides, C., Arnold, E., Horsfall, I., Tozzi, G., Gibson, M. C., & Zioupos, P. (2020). Spinal Motion Segments — I: Concept for a Subject-specific Analogue Model. Journal of Bionic Engineering, 17(4), 747-756. https://doi.org/10.1007/s42235-020-0060-1

• Journal Article Type: Article
• Online Publication Date: Jun 24, 2020
• Publication Date: Jul 1, 2020
• Deposit Date: Jul 19, 2023
• Journal: Journal of Bionic Engineering
• Print ISSN: 1672-6529
• Electronic ISSN: 2543-2141
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 4
• Pages: 747-756
• DOI: https://doi.org/10.1007/s42235-020-0060-1
• Public URL: https://hull-repository.worktribe.com/output/4287137
• Related Public URLs: https://dspace.lib.cranfield.ac.uk/handle/1826/15522