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

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...

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Autores principales: Garavelli, Chiara, Curreli, Cristina, Palanca, Marco, Aldieri, Alessandra, Cristofolini, Luca, Viceconti, Marco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9462677/
https://www.ncbi.nlm.nih.gov/pubmed/36084092
http://dx.doi.org/10.1371/journal.pone.0272529
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author Garavelli, Chiara
Curreli, Cristina
Palanca, Marco
Aldieri, Alessandra
Cristofolini, Luca
Viceconti, Marco
author_facet Garavelli, Chiara
Curreli, Cristina
Palanca, Marco
Aldieri, Alessandra
Cristofolini, Luca
Viceconti, Marco
author_sort Garavelli, Chiara
collection PubMed
description 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.
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spelling pubmed-94626772022-09-10 Experimental validation of a subject-specific finite element model of lumbar spine segment using digital image correlation Garavelli, Chiara Curreli, Cristina Palanca, Marco Aldieri, Alessandra Cristofolini, Luca Viceconti, Marco PLoS One Research Article 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. Public Library of Science 2022-09-09 /pmc/articles/PMC9462677/ /pubmed/36084092 http://dx.doi.org/10.1371/journal.pone.0272529 Text en © 2022 Garavelli et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Garavelli, Chiara
Curreli, Cristina
Palanca, Marco
Aldieri, Alessandra
Cristofolini, Luca
Viceconti, Marco
Experimental validation of a subject-specific finite element model of lumbar spine segment using digital image correlation
title Experimental validation of a subject-specific finite element model of lumbar spine segment using digital image correlation
title_full Experimental validation of a subject-specific finite element model of lumbar spine segment using digital image correlation
title_fullStr Experimental validation of a subject-specific finite element model of lumbar spine segment using digital image correlation
title_full_unstemmed Experimental validation of a subject-specific finite element model of lumbar spine segment using digital image correlation
title_short Experimental validation of a subject-specific finite element model of lumbar spine segment using digital image correlation
title_sort experimental validation of a subject-specific finite element model of lumbar spine segment using digital image correlation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9462677/
https://www.ncbi.nlm.nih.gov/pubmed/36084092
http://dx.doi.org/10.1371/journal.pone.0272529
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