Subject-specific knee ligaments modeling approaches in finite element analysis: 1D and 3D

Knee ligaments are among the most complicated structures and have a large effect on knee biomechanics. There are different approaches to model the knee ligaments in FE models. In the knee joint, ligaments have been commonly modelled as 1D spring elements; moreover, some studies modelled the ligaments as 3D constitutive elements [2]. Using springs reduces computational costs compared to constitutive models of the ligaments. In turn, constitutive models closer approximate the anatomy, and facilitate the prediction of local quantities and interactions with surrounding tissues, such as wrapping [1]. To the best of our knowledge, there is no direct/practical comparison study between two FE ligament modelling approaches. The aim of this study is to develop and compare two separate subject-specific finite element knee models in terms of ligament modelling approaches, based on cadaveric validation experiments

Toward patient-specific estimation of hepatic tumors respiratory motion: A finite element-based machine learning approach

Purpose To develop and evaluate an approach to estimate the respiratory-induced motion of hepatic lesions. Materials and methods In this study, a correlation between the collected surrogate signals and the liver tumor respiratory motion is obtained using learning-based algorithms. A robotic phantom is developed which simulates the respiratory motion of the liver, the diaphragm, and the abdomen skin in two directions as superior-inferior (SI) and anterior-posterior (AP). The surrogate signals are collected by means of optical markers attached to the abdomen skin and tracked by a digital camera, in addition to an inertial measurement unit (IMU) fixed to the hub of a needle that is inserted into the tissue. A finite element model (FEM) is developed to study the effect of tissue and tumor location parameters on target motion. Results The estimation error of linear regression for the SI and AP directions has been respectively 1.37% and 2.87%, and for quadratic polynomial regression have been 0.76% and 2.41% on the data from the experiments. Conclusion Using more than one surrogate signals resulted in a about 0.5-6.5% decrease of the motion estimation error compared to using only one of the surrogates. Keywords: hepatic tumor, motion estimation, respiratory motion, machine learning, robotic phantom, finite element analysi

The peripheral soft tissues should not be ignored in the finite element models of the human knee joint

In finite element models of the either implanted or intact human knee joint, soft tissue structures like tendons and ligaments are being incorporated, but usually skin, peripheral knee soft tissues, and the posterior capsule are ignored and assumed to be of minor influence on knee joint biomechanics. It is, however, unknown how these peripheral structures influence the biomechanical response of the knee. In this study, the aim was to assess the significance of the peripheral soft tissues and posterior capsule on the kinematics and laxities of human knee joint, based on experimental tests on three human cadaveric specimens. Despite the high inter-subject variability of the results, it was demonstrated that the target tissues have a considerable influence on posterior translational and internal and valgus rotational laxities of lax knees under flexion. Consequently, ignoring these tissues from computational models may alter the knee joint biomechanics