Subject‐specific biomechanical analysis to estimate locations susceptible to osteoarthritis—Finite element modeling and MRI follow‐up of ACL reconstructed patients

Abstract

The aims of this case-control study were to: (1) Identify cartilage locations and volumes at risk of osteoarthritis (OA) using subject-specific finite element (FE) models; (2) Quantify the relationships between the simulated biomechanical parameters and T 2 and T 1ρ relaxation times of magnetic resonance imaging (MRI). We created subject-specific FE models for seven patients with anterior cruciate ligament (ACL) reconstruction and six controls based on a previous proof-of-concept study. We identified locations and cartilage volumes susceptible to OA, based on maximum principal stresses and absolute maximum shear strains in cartilage exceeding thresholds of 7 MPa and 32%, respectively. The locations and volumes susceptible to OA were compared qualitatively and quantitatively against 2-year longitudinal changes in T 2 and T 1ρ relaxation times. The degeneration volumes predicted by the FE models, based on excessive maximum principal stresses, were significantly correlated (r = 0.711, p < 0.001) with the degeneration volumes determined from T 2 relaxation times. There was also a significant correlation between the predicted stress values and changes in T 2 relaxation time (r = 0.649, p < 0.001). Absolute maximum shear strains and changes in T 1ρ relaxation time were not significantly correlated. Five out of seven patients with ACL reconstruction showed excessive maximum principal stresses in either one or both tibial

Optimized simplified constitutive models with an atlas-based finite element analysis can be utilized to predict personalized progression of knee osteoarthritis: Data from the Osteoarthritis Initiative

“…In approaches based on predictions generated from nite element analysis [8]- [10], the mechanical response of the cartilage tissue within the joint is simulated and combined with degenerative algorithms that include a mechanical threshold(s) beyond which tissue degeneration takes place. Similarly, as with ML approaches, FEA approaches have its own limitations to be utilized as a part of clinical evaluation.…”

“…Recently, also OA research has increasingly started focusing on the development of different approaches and methods based on machine learning (ML) algorithms [4]- [7] and nite element analysis (FEA) [8]- [10] to classify subjects at high risk for knee OA development. Especially, the aim has been to identify the high-risk subjects before any degenerative signs are detected from a clinical image.…”

Effects of gait modifications on tissue‐level knee mechanics in individuals with medial tibiofemoral osteoarthritis: A proof‐of‐concept study towards personalized interventions

“…Hence, the material parameters of the FE models were adopted from the literature, including our previously verified MS‐FE models. Nonetheless, it has been reported that the use of softer or stiffer materials (i.e., representative of healthy or osteoarthritic cartilage) may change the magnitude of the estimated tissue mechanics, but the pattern and distribution of tissue mechanics remain comparable 54 . Importantly, our investigations in the current study did not focus on the magnitudes of the estimated tissue mechanics, but we compared the relative tissue mechanics across the undertaken gait modifications.…”

“…First, we did not evaluate the effect of long‐term use of gait modifications, which may alter the outputs. Nevertheless, the utilized workflow has shown potential for analysis of follow‐up assessments 14,54 . Also, some gait modifications (i.e., toe‐in, toe‐out, and wide stance) were not standardized, as this would be difficult to achieve in practice.…”

Finite element modeling with subject‐specific mechanical properties to assess knee osteoarthritis initiation and progression

“…used in this study was not very strong. 19 While new studies are emerging which connect biomechanical measures and T 2 relaxation, 50 more research needs to be done to elucidate the relationship between T 2 relaxation and tissue material properties. Finally, we realize that there is an inherent tradeoff between material specificity and model generalizability.…”