Image, geometry and finite element mesh datasets for analysis of relationship between abdominal aortic aneurysm symptoms and stress in walls of abdominal aortic aneurysm.

These datasets contain Computed Tomography (CT) images of 19 patients with Abdominal Aortic Aneurysm (AAA) together with 19 patient-specific geometry data and computational grids (finite element meshes) created from these images applied in the research reported in Journal of Surgical Research article "Is There A Relationship Between Stress in Walls of Abdominal Aortic Aneurysm and Symptoms?"[1]. The images were randomly selected from the retrospective database of University Hospitals Leuven (Leuven, Belgium) and provided to The University of Western Australia's Intelligent Systems for Medicine Laboratory. The analysis was conducted using our freely-available open-source software BioPARR (Joldes et al.

A simple method of incorporating the effect of the Uniform Stress Hypothesis in arterial wall stress computations.

Purpose: Residual stress has a great influence on the mechanical behaviour of arterial wall. Numerous research groups used the Uniform Stress Hypothesis to allow the inclusion of the effects of residual stress when computing stress distributions in the arterial wall. Nevertheless, the available methods used for this purpose are very computationally expensive, due to their iterative nature.

BioPARR: A software system for estimating the rupture potential index for abdominal aortic aneurysms.

An abdominal aortic aneurysm (AAA) is a permanent and irreversible dilation of the lower region of the aorta. It is a symptomless condition that, if left untreated, can expand until rupture. Despite ongoing efforts, an efficient tool for accurate estimation of AAA rupture risk is still not available.

Biomechanical model for computing deformations for whole-body image registration: A meshless approach.

Patient-specific biomechanical models have been advocated as a tool for predicting deformations of soft body organs/tissue for medical image registration (aligning two sets of images) when differences between the images are large. However, complex and irregular geometry of the body organs makes generation of patient-specific biomechanical models very time-consuming. Meshless discretisation has been proposed to solve this challenge.

From Finite Element Meshes to Clouds of Points: A Review of Methods for Generation of Computational Biomechanics Models for Patient-Specific Applications.

It has been envisaged that advances in computing and engineering technologies could extend surgeons' ability to plan and carry out surgical interventions more accurately and with less trauma. The progress in this area depends crucially on the ability to create robustly and rapidly patient-specific biomechanical models. We focus on methods for generation of patient-specific computational grids used for solving partial differential equations governing the mechanics of the body organs.

A simple, effective and clinically applicable method to compute abdominal aortic aneurysm wall stress.

Abdominal aortic aneurysm (AAA) is a permanent and irreversible dilation of the lower region of the aorta. It is a symptomless condition that if left untreated can expand to the point of rupture. Mechanically-speaking, rupture of an artery occurs when the local wall stress exceeds the local wall strength.

Towards measuring neuroimage misalignment.

To enhance neuro-navigation, high quality pre-operative images must be registered onto intra-operative configuration of the brain. Therefore evaluation of the degree to which structures may remain misaligned after registration is critically important. We consider two Hausdorff Distance (HD)-based evaluation approaches: the edge-based HD (EBHD) metric and the Robust HD (RHD) metric as well as various commonly used intensity-based similarity metrics such as Mutual Information (MI), Normalised Mutual Information (NMI), Entropy Correlation Coefficient (ECC), Kullback-Leibler Distance (KLD) and Correlation Ratio (CR).

Patient-specific biomechanical model as whole-body CT image registration tool.

Whole-body computed tomography (CT) image registration is important for cancer diagnosis, therapy planning and treatment. Such registration requires accounting for large differences between source and target images caused by deformations of soft organs/tissues and articulated motion of skeletal structures. The registration algorithms relying solely on image processing methods exhibit deficiencies in accounting for such deformations and motion.

More accurate neuronavigation data provided by biomechanical modeling instead of rigid registration.

It is possible to improve neuronavigation during image-guided surgery by warping the high-quality preoperative brain images so that they correspond with the current intraoperative configuration of the brain. In this paper, the accuracy of registration results obtained using comprehensive biomechanical models is compared with the accuracy of rigid registration, the technology currently available to patients. This comparison allows investigation into whether biomechanical modeling provides good-quality image data for neuronavigation for a larger proportion of patients than rigid registration.

Biomechanical model as a registration tool for image-guided neurosurgery: evaluation against BSpline registration.

In this paper we evaluate the accuracy of warping of neuro-images using brain deformation predicted by means of a patient-specific biomechanical model against registration using a BSpline-based free form deformation algorithm. Unlike the BSpline algorithm, biomechanics-based registration does not require an intra-operative MR image which is very expensive and cumbersome to acquire. Only sparse intra-operative data on the brain surface is sufficient to compute deformation for the whole brain.

Cellular automata coupled with steady-state nutrient solution permit simulation of large-scale growth of tumours.

We model complete growth of an avascular tumour by employing cellular automata for the growth of cells and steady-state equation to solve for nutrient concentrations. Our modelling and computer simulation results show that, in the case of a brain tumour, oxygen distribution in the tumour volume may be sufficiently described by a time-independent steady-state equation without losing the characteristics of a time-dependent diffusion equation. This makes the solution of oxygen concentration in the tumour volume computationally more efficient, thus enabling simulation of tumour growth on a large scale.

Patient-specific computational biomechanics of the brain without segmentation and meshing.

Motivated by patient-specific computational modelling in the context of image-guided brain surgery, we propose a new fuzzy mesh-free modelling framework. The method works directly on an unstructured cloud of points that do not form elements so that mesh generation is not required. Mechanical properties are assigned directly to each integration point based on fuzzy tissue classification membership functions without the need for image segmentation.

Meshless algorithm for soft tissue cutting in surgical simulation.

Computation of soft tissue mechanical responses for surgery simulation and image-guided surgery has been dominated by the finite element (FE) method that utilises a mesh of interconnected elements as a computational grid. Shortcomings of such mesh-based discretisation in modelling of surgical cutting include high computational cost and the need for re-meshing in the vicinity of cutting-induced discontinuity. The meshless total Lagrangian adaptive dynamic relaxation (MTLADR) algorithm we present here does not exhibit such shortcomings, as it relies on spatial discretisation in a form of a cloud of nodes.

Neurosurgery Simulation Using Non-linear Finite Element Modeling and Haptic Interaction.

Real-time surgical simulation is becoming an important component of surgical training. To meet the real-time requirement, however, the accuracy of the biomechancial modeling of soft tissue is often compromised due to computing resource constraints. Furthermore, haptic integration presents an additional challenge with its requirement for a high update rate.

An adaptive Dynamic Relaxation method for solving nonlinear finite element problems. Application to brain shift estimation.

Dynamic Relaxation is an explicit method that can be used for computing the steady state solution for a discretised continuum mechanics problem. The convergence speed of the method depends on the accurate estimation of the parameters involved, which is especially difficult for nonlinear problems. In this paper we propose a completely adaptive Dynamic Relaxation method in which the parameters are updated during the iteration process, converging to their optimal values.

Real-Time Nonlinear Finite Element Computations on GPU - Application to Neurosurgical Simulation.

Application of biomechanical modeling techniques in the area of medical image analysis and surgical simulation implies two conflicting requirements: accurate results and high solution speeds. Accurate results can be obtained only by using appropriate models and solution algorithms. In our previous papers we have presented algorithms and solution methods for performing accurate nonlinear finite element analysis of brain shift (which includes mixed mesh, different non-linear material models, finite deformations and brain-skull contacts) in less than a minute on a personal computer for models having up to 50.

Computation of intra-operative brain shift using dynamic relaxation.

Many researchers have proposed the use of biomechanical models for high accuracy soft organ non-rigid image registration, but one main problem in using comprehensive models is the long computation time required to obtain the solution. In this paper we propose to use the Total Lagrangian formulation of the Finite Element method together with Dynamic Relaxation for computing intra-operative organ deformations. We study the best ways of estimating the parameters involved and we propose a termination criteria that can be used in order to obtain fast results with prescribed accuracy.

Non-locking Tetrahedral Finite Element for Surgical Simulation.

To obtain a very fast solution for finite element models used in surgical simulations low order elements such as the linear tetrahedron or the linear under-integrated hexahedron must be used. Automatic hexahedral mesh generation for complex geometries remains a challenging problem, and therefore tetrahedral or mixed meshes are often necessary. Unfortunately the standard formulation of the linear tetrahedral element exhibits volumetric locking in case of almost incompressible materials.

Suite of finite element algorithms for accurate computation of soft tissue deformation for surgical simulation.

Real time computation of soft tissue deformation is important for the use of augmented reality devices and for providing haptic feedback during operation or surgeon training. This requires algorithms that are fast, accurate and can handle material nonlinearities and large deformations. A set of such algorithms is presented in this paper, starting with the finite element formulation and the integration scheme used and addressing common problems such as hourglass control and locking.

Real-time prediction of brain shift using nonlinear finite element algorithms.

Patient-specific biomechanical models implemented using specialized nonlinear (i.e., taking into account material and geometric nonlinearities) finite element procedures were applied to predict the deformation field within the brain for five cases of craniotomy-induced brain shift.