Probabilistic evaluation of the material properties of the in vivo subject-specific articular surface using a computational model

Kyoung Tak Kang, Sung Hwan Kim, Juhyun Son, Young Han Lee, Shinil Kim, Heoung Jae Chun

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

This article used probabilistic analysis to evaluate material properties of the in vivo subject-specific tibiofemoral (TF) joint model. Sensitivity analysis, based on a Monte Carlo (MC) method, was performed using a subject-specific finite element (FE) model generated from in vivo computed tomography (CT) and magnetic resonance imaging (MRI) data, subjected to two different loading conditions. Specifically, the effects of inherent uncertainty in ligament stiffness, horn attachment stiffness, and articular surface material properties were assessed using multifactorial global sensitivity analysis. The MRI images were taken before and after axial compression, and when the flexion condition had been maintained at up to 90 degree flexion in the subject-specific knee joint. The loading conditions of the probabilistic subject-specific FE model (axial compression and 90 degree flexion) were similar to the MRI acquisition setup. We were able to detect the influence of material parameters while maintaining the potential effect of parametric interactions. Throughout the in silico property optimization, a subject-specific FE model was used and less sensitive parameters were eliminated in the global sensitivity method. Soft tissue material properties were estimated using an optimization procedure that involved the minimization of the differences between the kinematics predicted by the subject-specific model and those obtained through in vivo subject-specific data. The results of this approach suggest that the articular surface mechanical properties could be found by using in vivo measurements, which clarifies the valuable tool for future subject-specific studies related to TF joint scaffolds, allografts and biologics.

Original languageEnglish
Pages (from-to)1390-1400
Number of pages11
JournalJournal of Biomedical Materials Research - Part B Applied Biomaterials
Volume105
Issue number6
DOIs
Publication statusPublished - 2017 Aug

All Science Journal Classification (ASJC) codes

  • Biomaterials
  • Biomedical Engineering

Fingerprint Dive into the research topics of 'Probabilistic evaluation of the material properties of the in vivo subject-specific articular surface using a computational model'. Together they form a unique fingerprint.

  • Cite this