Hyperelastic modelling and parametric study of soft tissue embedded lump for MIS applications

Computational simulation of breast compression based on segmented breast and fibroglandular tissues on magnetic resonance images

This study presents a finite element-based computational model to simulate the three-dimensional deformation of a breast and fibroglandular tissues under compression. The simulation was based on 3D MR images of the breast, and craniocaudal and mediolateral oblique compression, as used in mammography, was applied. The geometry of the whole breast and the segmented fibroglandular tissues within the breast were reconstructed using triangular meshes by using the Avizo 6.0 software package. Due to the large deformation in breast compression, a finite element model was used to simulate the nonlinear elastic tissue deformation under compression, using the MSC.Marc software package. The model was tested in four cases. The results showed a higher displacement along the compression direction compared to the other two directions. The compressed breast thickness in these four cases at a compression ratio of 60% was in the range of 5-7 cm, which is a typical range of thickness in mammography. The projection of the fibroglandular tissue mesh at a compression ratio of 60% was compared to the corresponding mammograms of two women, and they demonstrated spatially matched distributions. However, since the compression was based on magnetic resonance imaging (MRI), which has much coarser spatial resolution than the in-plane resolution of mammography, this method is unlikely to generate a synthetic mammogram close to the clinical quality. Whether this model may be used to understand the technical factors that may impact the variations in breast density needs further investigation. Since this method can be applied to simulate compression of the breast at different views and different compression levels, another possible application is to provide a tool for comparing breast images acquired using different imaging modalities--such as MRI, mammography, whole breast ultrasound and molecular imaging--that are performed using different body positions and under different compression conditions.

Advances in collision detection and non-linear finite mixed element modelling for improved soft tissue simulation in craniomaxillofacial surgical planning

BACKGROUND

There is a huge demand to develop a method for assisting surgeons in automatically predicting soft tissue deformation in terms of a bone-remodelling plan.

METHODS

This paper introduces several novel elements into a system for the simulation of postoperative facial appearances with respect to prespecified bone-remodelling plans. First, a new algorithm for efficient detection of collisions, using the signed distance field, is described. Next, the penalty method is applied to determine the contact load of bone on facial soft tissue. Finally, a non-linear finite mixed element model is developed to estimate the tissue deformation induced by the prescribed bone remodelling plan.

RESULTS

The performance of the proposed collision detection algorithm has been improved in memory requirements and computational efficiency compared with conventional methods. In addition, the methodology is evaluated over both synthetic and real data, with simulation performance averaging <0.5 mm pointwise error over the facial surface in six mid-face distraction osteotogenesis procedures.

CONCLUSIONS

The experimental results support the novel methodological advancements in collision detection and biomechanical modelling proposed in this work.

An improved finite element model for craniofacial surgery simulation

PURPOSE

A novel approach is proposed for simulating the deformation of the facial soft tissues in the craniofacial surgery simulation.

METHODS

A nonlinear finite mixed-element model (NFM-EM) based on solid-shell elements and Lagrange principle of virtual work is proposed, which addresses the heterogeneity in geometry and material properties found in the soft tissues of the face. Moreover, after the investigation of the strain-potential models, the biomechanical characteristics of skin, muscles and fat are modeled with the most suitable material properties. In addition, an improved contact algorithm is used to compute the boundary conditions of the soft tissue model.

RESULTS

The quantitative validation and the comparative results with other models proved the effectiveness of the approach on the simulation of complex soft tissues. The average absolute value of errors stays below 0.5 mm and the 95% percentiles of the distance map is less than 1.5 mm.

CONCLUSIONS

NFM-EM promotes the accuracy and effectiveness of the soft tissue deformation, and the effective contact algorithm bridges the bone-related planning and the prediction of the target face.