19,227 research outputs found
Modelling Rod-like Flexible Biological Tissues for Medical Training
This paper outlines a framework for the modelling of slender rod-like biological tissue structures in both global and local scales. Volumetric discretization of a rod-like structure is expensive in computation and therefore
is not ideal for applications where real-time performance is essential. In our approach, the Cosserat rod model is introduced to capture the global shape changes, which models the structure as a one-dimensional entity, while the
local deformation is handled separately. In this way a good balance in accuracy and efficiency is achieved. These advantages make our method appropriate for
the modelling of soft tissues for medical training applications
Simulation of hyperelastic materials in real-time using Deep Learning
The finite element method (FEM) is among the most commonly used numerical
methods for solving engineering problems. Due to its computational cost,
various ideas have been introduced to reduce computation times, such as domain
decomposition, parallel computing, adaptive meshing, and model order reduction.
In this paper we present U-Mesh: a data-driven method based on a U-Net
architecture that approximates the non-linear relation between a contact force
and the displacement field computed by a FEM algorithm. We show that deep
learning, one of the latest machine learning methods based on artificial neural
networks, can enhance computational mechanics through its ability to encode
highly non-linear models in a compact form. Our method is applied to two
benchmark examples: a cantilever beam and an L-shape subject to moving punctual
loads. A comparison between our method and proper orthogonal decomposition
(POD) is done through the paper. The results show that U-Mesh can perform very
fast simulations on various geometries, mesh resolutions and number of input
forces with very small errors
A Study of Speed of the Boundary Element Method as applied to the Realtime Computational Simulation of Biological Organs
In this work, possibility of simulating biological organs in realtime using
the Boundary Element Method (BEM) is investigated. Biological organs are
assumed to follow linear elastostatic material behavior, and constant boundary
element is the element type used. First, a Graphics Processing Unit (GPU) is
used to speed up the BEM computations to achieve the realtime performance.
Next, instead of the GPU, a computer cluster is used. Results indicate that BEM
is fast enough to provide for realtime graphics if biological organs are
assumed to follow linear elastostatic material behavior. Although the present
work does not conduct any simulation using nonlinear material models, results
from using the linear elastostatic material model imply that it would be
difficult to obtain realtime performance if highly nonlinear material models
that properly characterize biological organs are used. Although the use of BEM
for the simulation of biological organs is not new, the results presented in
the present study are not found elsewhere in the literature.Comment: preprint, draft, 2 tables, 47 references, 7 files, Codes that can
solve three dimensional linear elastostatic problems using constant boundary
elements (of triangular shape) while ignoring body forces are provided as
supplementary files; codes are distributed under the MIT License in three
versions: i) MATLAB version ii) Fortran 90 version (sequential code) iii)
Fortran 90 version (parallel code
Real-time Error Control for Surgical Simulation
Objective: To present the first real-time a posteriori error-driven adaptive
finite element approach for real-time simulation and to demonstrate the method
on a needle insertion problem. Methods: We use corotational elasticity and a
frictional needle/tissue interaction model. The problem is solved using finite
elements within SOFA. The refinement strategy relies upon a hexahedron-based
finite element method, combined with a posteriori error estimation driven local
-refinement, for simulating soft tissue deformation. Results: We control the
local and global error level in the mechanical fields (e.g. displacement or
stresses) during the simulation. We show the convergence of the algorithm on
academic examples, and demonstrate its practical usability on a percutaneous
procedure involving needle insertion in a liver. For the latter case, we
compare the force displacement curves obtained from the proposed adaptive
algorithm with that obtained from a uniform refinement approach. Conclusions:
Error control guarantees that a tolerable error level is not exceeded during
the simulations. Local mesh refinement accelerates simulations. Significance:
Our work provides a first step to discriminate between discretization error and
modeling error by providing a robust quantification of discretization error
during simulations.Comment: 12 pages, 16 figures, change of the title, submitted to IEEE TBM
A fast and robust patient specific Finite Element mesh registration technique: application to 60 clinical cases
Finite Element mesh generation remains an important issue for patient
specific biomechanical modeling. While some techniques make automatic mesh
generation possible, in most cases, manual mesh generation is preferred for
better control over the sub-domain representation, element type, layout and
refinement that it provides. Yet, this option is time consuming and not suited
for intraoperative situations where model generation and computation time is
critical. To overcome this problem we propose a fast and automatic mesh
generation technique based on the elastic registration of a generic mesh to the
specific target organ in conjunction with element regularity and quality
correction. This Mesh-Match-and-Repair (MMRep) approach combines control over
the mesh structure along with fast and robust meshing capabilities, even in
situations where only partial organ geometry is available. The technique was
successfully tested on a database of 5 pre-operatively acquired complete femora
CT scans, 5 femoral heads partially digitized at intraoperative stage, and 50
CT volumes of patients' heads. The MMRep algorithm succeeded in all 60 cases,
yielding for each patient a hex-dominant, Atlas based, Finite Element mesh with
submillimetric surface representation accuracy, directly exploitable within a
commercial FE software
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