4,060 research outputs found
Real-time elastic deformations of soft tissues for surgery simulation
International audienceWe describe a novel method for surgery simulation including a volumetric model built from medical images and an elastic modeling of the deformations. The physical model is based on elasticity theory which suitably links the shape of deformable bodies and the forces associated with the deformation. A real time computation of the deformation is possible thanks to a preprocessing of elementary deformations derived from a finite element method. This method has been implemented in a system including a force feedback device and a collision detection algorithm. The simulator works in real time with a high resolution liver mode
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
A 3D discrete model of the diaphragm and human trunk
In this paper, a 3D discrete model is presented to model the movements of the
trunk during breathing. In this model, objects are represented by physical
particles on their contours. A simple notion of force generated by a linear
actuator allows the model to create forces on each particle by way of a
geometrical attractor. Tissue elasticity and contractility are modeled by local
shape memory and muscular fibers attractors. A specific dynamic MRI study was
used to build a simple trunk model comprised of by three compartments: lungs,
diaphragm and abdomen. This model was registered on the real geometry.
Simulation results were compared qualitatively as well as quantitatively to the
experimental data, in terms of volume and geometry. A good correlation was
obtained between the model and the real data. Thanks to this model, pathology
such as hemidiaphragm paralysis can also be simulated.Comment: published in: "Lung Modelling", France (2006
In vivo measurement of human brain elasticity using a light aspiration device
The brain deformation that occurs during neurosurgery is a serious issue
impacting the patient "safety" as well as the invasiveness of the brain
surgery. Model-driven compensation is a realistic and efficient solution to
solve this problem. However, a vital issue is the lack of reliable and easily
obtainable patient-specific mechanical characteristics of the brain which,
according to clinicians' experience, can vary considerably. We designed an
aspiration device that is able to meet the very rigorous sterilization and
handling process imposed during surgery, and especially neurosurgery. The
device, which has no electronic component, is simple, light and can be
considered as an ancillary instrument. The deformation of the aspirated tissue
is imaged via a mirror using an external camera. This paper describes the
experimental setup as well as its use during a specific neurosurgery. The
experimental data was used to calibrate a continuous model. We show that we
were able to extract an in vivo constitutive law of the brain elasticity: thus
for the first time, measurements are carried out per-operatively on the
patient, just before the resection of the brain parenchyma. This paper
discloses the results of a difficult experiment and provide for the first time
in-vivo data on human brain elasticity. The results point out the softness as
well as the highly non-linear behavior of the brain tissue.Comment: Medical Image Analysis (2009) accept\'
NOViSE: a virtual natural orifice transluminal endoscopic surgery simulator
Purpose: Natural Orifice Transluminal Endoscopic Surgery (NOTES) is a novel technique in minimally invasive surgery whereby a flexible endoscope is inserted via a natural orifice to gain access to the abdominal cavity, leaving no external scars. This innovative use of flexible endoscopy creates many new challenges and is associated with a steep learning curve for clinicians. Methods: We developed NOViSE - the first force-feedback enabled virtual reality simulator for NOTES training supporting a flexible endoscope. The haptic device is custom built and the behaviour of the virtual flexible endoscope is based on an established theoretical framework – the Cosserat Theory of Elastic Rods. Results: We present the application of NOViSE to the simulation of a hybrid trans-gastric cholecystectomy procedure. Preliminary results of face, content and construct validation have previously shown that NOViSE delivers the required level of realism for training of endoscopic manipulation skills specific to NOTES Conclusions: VR simulation of NOTES procedures can contribute to surgical training and improve the educational experience without putting patients at risk, raising ethical issues or requiring expensive animal or cadaver facilities. In the context of an experimental technique, NOViSE could potentially facilitate NOTES development and contribute to its wider use by keeping practitioners up to date with this novel surgical technique. NOViSE is a first prototype and the initial results indicate that it provides promising foundations for further development
Medical image computing and computer-aided medical interventions applied to soft tissues. Work in progress in urology
Until recently, Computer-Aided Medical Interventions (CAMI) and Medical
Robotics have focused on rigid and non deformable anatomical structures.
Nowadays, special attention is paid to soft tissues, raising complex issues due
to their mobility and deformation. Mini-invasive digestive surgery was probably
one of the first fields where soft tissues were handled through the development
of simulators, tracking of anatomical structures and specific assistance
robots. However, other clinical domains, for instance urology, are concerned.
Indeed, laparoscopic surgery, new tumour destruction techniques (e.g. HIFU,
radiofrequency, or cryoablation), increasingly early detection of cancer, and
use of interventional and diagnostic imaging modalities, recently opened new
challenges to the urologist and scientists involved in CAMI. This resulted in
the last five years in a very significant increase of research and developments
of computer-aided urology systems. In this paper, we propose a description of
the main problems related to computer-aided diagnostic and therapy of soft
tissues and give a survey of the different types of assistance offered to the
urologist: robotization, image fusion, surgical navigation. Both research
projects and operational industrial systems are discussed
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
Finite element model set-up of colorectal tissue for analyzing surgical scenarios
Finite Element Analysis (FEA) has gained an extensive application in the medical field, such as soft tissues simulations. In particular, colorectal simulations can be used to understand the interaction with the surrounding tissues, or with instruments used in surgical procedures. Although several works have been introduced considering small displacements, as a result of the forces exerted on adjacent tissues, FEA applied to colorectal surgical scenarios is still a challenge. Therefore, this work aims to provide a sensitivity analysis on three geometric models, taking in mind different bioengineering tasks. In this way, a set of simulations has been performed using three mechanical models named Linear Elastic, Hyper-Elastic with a Mooney-Rivlin material model, and Hyper-Elastic with a YEOH material model
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