A numerical model for fibril remodeling in articular cartilage

Kakavand, Reza; Rasoulian, Akbar; Otoo, Baaba S; Komeili, Amin

doi:10.1016/j.knee.2022.12.009

Cited by 1 publication

(1 citation statement)

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“…The results of creep indentation experiments conducted by Pajerski 46 and Athanasiou et al 47 on human knee cartilage served as the basis for the extracellular matrix (ECM) material www.nature.com/scientificreports/ properties 46 . A detailed explanation of the cartilage constitutive laws with its material constants used for describing cartilage behavior can be found in the supplementary material and also in our previous work 48,49 . Briefly, the state of stress was defined by: where σ is the total stress in the tissue, p is the hydrostatic interstitial fluid pressure, I is the unity tensor, and ∅ is the volume fraction.…”

Section: Materials and Finite Element Modelingmentioning

confidence: 99%

Integration of Swin UNETR and statistical shape modeling for a semi-automated segmentation of the knee and biomechanical modeling of articular cartilage

Kakavand,

Palizi,

Tahghighi

et al. 2024

Sci Rep

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Simulation studies, such as finite element (FE) modeling, provide insight into knee joint mechanics without patient involvement. Generic FE models mimic the biomechanical behavior of the tissue, but overlook variations in geometry, loading, and material properties of a population. Conversely, subject-specific models include these factors, resulting in enhanced predictive precision, but are laborious and time intensive. The present study aimed to enhance subject-specific knee joint FE modeling by incorporating a semi-automated segmentation algorithm using a 3D Swin UNETR for an initial segmentation of the femur and tibia, followed by a statistical shape model (SSM) adjustment to improve surface roughness and continuity. For comparison, a manual FE model was developed through manual segmentation (i.e., the de-facto standard approach). Both FE models were subjected to gait loading and the predicted mechanical response was compared. The semi-automated segmentation achieved a Dice similarity coefficient (DSC) of over 98% for both the femur and tibia. Hausdorff distance (mm) between the semi-automated and manual segmentation was 1.4 mm. The mechanical results (max principal stress and strain, fluid pressure, fibril strain, and contact area) showed no significant differences between the manual and semi-automated FE models, indicating the effectiveness of the proposed semi-automated segmentation in creating accurate knee joint FE models. We have made our semi-automated models publicly accessible to support and facilitate biomechanical modeling and medical image segmentation efforts (https://data.mendeley.com/datasets/k5hdc9cz7w/1).

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Section: Materials and Finite Element Modelingmentioning

confidence: 99%