Experimental validation of a subject-specific finite element model of lumbar spine segment using digital image correlation

• Published in: PloS one Vol. 17; no. 9; p. e0272529
• Main Authors: Garavelli, Chiara, Curreli, Cristina, Palanca, Marco, Aldieri, Alessandra, Cristofolini, Luca, Viceconti, Marco
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 09.09.2022; Public Library of Science (PLoS)
• Subjects: Biology and Life Sciences; Biomechanics; Bone density; Boundary conditions; Cadavers; Clinical medicine; Compression; Computed tomography; Correlation; Digital imaging; Disease; Displacement; Evaluation; Finite element method; Fractures; Fragility; Kinematics; Loading; Mathematical models; Measurement techniques; Mechanical loading; Mechanical properties; Medical imaging; Medicine and Health Sciences; Metastases; Metastasis; Modelling; Osteoporosis; Patients; Physical Sciences; Physiology; Research and Analysis Methods; Segments; Spine; Spine (lumbar); Tomography; Vertebra; Vertebrae; Italy
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0272529

Pathologies such as cancer metastasis and osteoporosis strongly affect the mechanical properties of the vertebral bone and increase the risk of fragility fractures. The prediction of the fracture risk with a patient-specific model, directly generated from the diagnostic images of the patient, could help the clinician in the choice of the correct therapy to follow. But before such models can be used to support any clinical decision, their credibility must be demonstrated through verification, validation, and uncertainty quantification. In this study we describe a procedure for the generation of such patient-specific finite element models and present a first validation of the kinematics of the spine segment. Quantitative computed tomography images of a cadaveric lumbar spine segment presenting vertebral metastatic lesions were used to generate the model. The applied boundary conditions replicated a specific experimental test where the spine segment was loaded in compression-flexion. Model predictions in terms of vertebral surface displacements were compared against the full-field experimental displacements measured with Digital Image Correlation. A good agreement was obtained from the local comparison between experimental data and simulation results (R 2 > 0.9 and RMSE% <8%). In conclusion, this work demonstrates the possibility to apply the developed modelling pipeline to predict the displacement field of human spine segment under physiological loading conditions, which is a first fundamental step in the credibility assessment of these clinical decision-support technology.