Template models for simulation of surface manipulation of musculoskeletal extremities

Abstract

Capturing the surface mechanics of musculoskeletal extremities would enhance the realism of life-like mechanics imposed on the limbs within surgical simulations haptics. Other fields that rely on surface manipulation, such as garment or prosthetic design, would also benefit from characterization of tissue surface mechanics. Eight homogeneous tissue models were developed for the upper and lower legs and arms of two donors. Ultrasound indentation data was used to drive an inverse finite element analysis for individualized determination of region-specific material coefficients for the lumped tissue. A novel calibration strategy was implemented by using a ratio based adjustment of tissue properties from linear regression of model predicted and experimental responses. This strategy reduced requirement of simulations to an average of under four iterations. These free and open-source specimen-specific models can serve as templates for simulations focused on mechanical manipulations of limb surfaces.

Role of multi-layer tissue composition of musculoskeletal extremities for prediction of in vivo surface indentation response and layer deformations

“…While individual layer composition and/or mechanics likely explain the bulk surface indentation response of soft tissue, there are limited experimental studies that investigate the mechanistic relationships of the underlying tissue layers and their respective interactions. Much of the literature examines each layer (skin, fat, or muscle) individually [14][15][16], in a targeted manner, or as a bulk tissue where all the layers are combined and treated as one [11,17,18], which in turn eliminates the mechanical interaction between layers that may explain the resulting mechanical behavior on a layer specific level.…”

“…Identifying the appropriate material model coefficients is a challenge, and can fail to capture the inherent variability between different patients. Inverse finite element analysis offers the ability to extract patient-specific response data, but requires mechanical testing, which may not be feasible in clinical or field applications, and optimization, which leads to even more simulation iterations and time [18]. Given these drawbacks, linear statistical modeling becomes a useful tool to analyze and predict how musculoskeletal soft tissue responds to loading.…”