Probabilistic approach for determining the material properties of meniscal attachments in vivo using magnetic resonance imaging and a finite element model

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

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

The material properties of in vivo meniscal attachments were evaluated using a probabilistic finite element (FE) model and magnetic resonance imaging (MRI). MRI scans of five subjects were collected at full extension and 30°, 60°, and 90° flexion. One subject with radiographic evidence of no knee injury and four subjects with Kellgren-Lawrence score of 1 or 2 (two each) were recruited. Isovoxel sagittal three-dimensional cube sequences of the knee were acquired in extension and flexion. Menisci movement in flexion was investigated using sensitivity analysis based on the Monte Carlo method in order to generate a subject-specific FE model to evaluate significant factors. The material properties of horn attachment in the five-subject FE model were optimized to minimize the differences between meniscal movements in the FE model and MR images in flexion. We found no significant difference between normal and patient knees in flexion with regard to movement of anterior, posterior, medial, and lateral menisci or changes in height morphology. At 90° flexion, menisci movement was primarily influenced by posterior horn stiffness, followed by anterior horn stiffness, the transverse ligament, and posterior cruciate ligament. The optimized material properties model predictions for menisci motion were more accurate than the initial material properties model. The results of this approach suggest that the material properties of horn attachment, which affects the mobile characteristics of menisci, could be determined in vivo. Thus, this study establishes a basis for a future design method of attachment for tissue-engineered replacement menisci.

Original languageEnglish
Pages (from-to)1097-1107
Number of pages11
JournalJournal of Computational Biology
Volume22
Issue number12
DOIs
Publication statusPublished - 2015 Dec

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics

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