Back

Image-based parametric finite element modelling for studying contact mechanics in human knee joints

Readioff, R.; Seil, R.; Mouton, C.; Marks, L.; Barrera, O.

2023-09-10 bioengineering
10.1101/2023.09.07.556747 bioRxiv
Show abstract

PurposeThis study presents a framework for generating patient-specific finite element models, parameterised and optimised for contact mechanics from computed tomography (CT) scans, by avoiding the segmentation step usually employed to transform medical images into 3D models. Two morphological parameters affecting contact mechanics were investigated in the framework development: tibial cartilage thickness and tibial spine height. This study explores the effect of the interplay of these parameters in load sharing between meniscus and articulating cartilage, meniscal posterior and anterior roots strain and menisci kine-matics. MethodsMorphological measurements from four knee CT scans were collected, such as the maximum thickness of the tibial cartilage (ranging from 1.1 to 5.2 mm), the height of the tibial spine (ranging from 3.55 to 10.1 mm), and the width of the tibial plateau in both the coronal (ranging from 27.3 to 36.17 mm) and sagittal (ranging from 31.79 to 53.77 mm) planes. These measurements were taken for the lateral tibial plateau for both left and right knees. Subsequently, three finite element (FE) models were generated, comprising lateral tibial plateaus, lateral femoral condyle and lateral meniscus. The tibial cartilage thickness was kept at a constant value of 1 mm while varying the tibial spine height within the range measured from the CT images. This resulted in three FE models with varying spine heights, categorised as large (height = 7.42 mm), medium (height = 4.25 mm), and small (height = 1.63 mm) tibial spine heights. The menisci in the FE models were generated to be congruent with the tibial plateau. For the first time, this study advances the representations of the knee menisci microstructure in FE modelling, such that we have generated meniscus FE models with three layers of a hyperelastic model in which layer thickness and layer-specific hyperelastic material parameters are derived from our previous experimental work. ResultsThe load sharing between the meniscus and articular cartilage was not sensitive to the varying tibial spine heights. In all three FE models, cartilage carried more than 90% of the applied load. However, the meniscus kinematics and root strains varied considerably with changing tibial spine heights. The small tibial spine height model predicted the highest meniscus movements (8.12 and 9.33 mm in the radial and circumferential directions, respectively) and the highest root strain (21.92 and 22.19 mm/mm in the anterior and posterior roots, respectively). ConclusionOur framework can generate finite element models of patients knees using clinical data (i.e., CT scans) without the need for lengthy image segmentation. This process is not only time-efficient but also independent of imaging operators. The models converge quickly ({inverted question}30 minutes on 2 cores) using an implicit solver with non-linear geometry and have the capability to predict contact mechanics between the articulating surfaces, meniscus kinematics and root strains. The modelling strategy presented here can provide valuable insights into predicting changes in the mechanics of soft tissues in the knee joint. It is particularly useful for investigating injury and surgical mechanisms related to the meniscus.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
PLOS ONE
5266 papers in training set
Top 10%
18.6%
2
Journal of Biomechanical Engineering
20 papers in training set
Top 0.1%
14.9%
3
Annals of Biomedical Engineering
37 papers in training set
Top 0.1%
9.8%
4
Journal of Biomechanics
64 papers in training set
Top 0.2%
6.8%
50% of probability mass above
5
Journal of the Mechanical Behavior of Biomedical Materials
24 papers in training set
Top 0.1%
6.8%
6
Biomechanics and Modeling in Mechanobiology
29 papers in training set
Top 0.1%
6.3%
7
Bioengineering
29 papers in training set
Top 0.1%
4.4%
8
Scientific Reports
3612 papers in training set
Top 26%
4.1%
9
Acta Biomaterialia
92 papers in training set
Top 0.5%
3.2%
10
Royal Society Open Science
214 papers in training set
Top 2%
2.8%
11
Frontiers in Bioengineering and Biotechnology
98 papers in training set
Top 0.9%
2.1%
12
Journal of The Royal Society Interface
235 papers in training set
Top 2%
1.7%
13
Journal of Orthopaedic Research
21 papers in training set
Top 0.2%
1.7%
14
IEEE Transactions on Biomedical Engineering
40 papers in training set
Top 0.6%
1.7%
15
PeerJ
308 papers in training set
Top 6%
1.5%
16
PLOS Computational Biology
1863 papers in training set
Top 16%
1.3%
17
Computer Methods in Biomechanics and Biomedical Engineering
10 papers in training set
Top 0.1%
1.1%
18
International Journal for Numerical Methods in Biomedical Engineering
14 papers in training set
Top 0.2%
1.1%
19
IEEE Transactions on Neural Systems and Rehabilitation Engineering
49 papers in training set
Top 0.8%
1.0%
20
Computer Methods and Programs in Biomedicine
28 papers in training set
Top 1%
0.6%