Increasing the perceptual salience of relationships in parallel coordinate plots.

We present three extensions to parallel coordinates that increase the perceptual salience of relationships between axes in multivariate data sets: (1) luminance modulation maintains the ability to preattentively detect patterns in the presence of overplotting, (2) adding a one-vs.-all variable display highlights relationships between one variable and all others, and (3) adding a scatter plot within the parallel-coordinates display preattentively highlights clusters and spatial layouts without strongly interfering with the parallel-coordinates display. These techniques can be combined with one another and with existing extensions to parallel coordinates, and two of them generalize beyond cases with known-important axes. We applied these techniques to two real-world data sets (relativistic heavy-ion collision hydrodynamics and weather observations with statistical principal component analysis) as well as the popular car data set. We present relationships discovered in the data sets using these methods.

Exploring Ensemble Visualization.

An ensemble is a collection of related datasets. Each dataset, or member, of an ensemble is normally large, multidimensional, and spatio-temporal. Ensembles are used extensively by scientists and mathematicians, for example, by executing a simulation repeatedly with slightly different input parameters and saving the results in an ensemble to see how parameter choices affect the simulation. To draw inferences from an ensemble, scientists need to compare data both within and between ensemble members. We propose two techniques to support ensemble exploration and comparison: a pairwise sequential animation method that visualizes locally neighboring members simultaneously, and a screen door tinting method that visualizes subsets of members using screen space subdivision. We demonstrate the capabilities of both techniques, first using synthetic data, then with simulation data of heavy ion collisions in high-energy physics. Results show that both techniques are capable of supporting meaningful comparisons of ensemble data.

Integration of a Multigrid ODE solver into an open medical simulation framework.

In this paper, we present the implementation of a Multigrid ODE solver in SOFA framework. By combining the stability advantage of coarse meshes and the transient detail preserving virtue of fine meshes, Multigrid ODE solver computes more efficiently than classic ODE solvers based on a single level discretization. With the ever wider adoption of the SOFA framework in many surgical simulation projects, introducing this Multigrid ODE solver into SOFA's pool of ODE solvers shall benefit the entire community. This contribution potentially has broad ramifications in the surgical simulation research community, given that in a single-resolution system, a constitutively realistic interactive tissue response, which presupposes large elements, is in direct conflict with the need to represent clinically relevant critical tissues in the simulation, which are typically be comprised of small elements.

Comparative Visualization of Ensembles Using Ensemble Surface Slicing.

By definition, an ensemble is a set of surfaces or volumes derived from a series of simulations or experiments. Sometimes the series is run with different initial conditions for one parameter to determine parameter sensitivity. The understanding and identification of visual similarities and differences among the shapes of members of an ensemble is an acute and growing challenge for researchers across the physical sciences. More specifically, the task of gaining spatial understanding and identifying similarities and differences between multiple complex geometric data sets simultaneously has proved challenging. This paper proposes a comparison and visualization technique to support the visual study of parameter sensitivity. We present a novel single-image view and sampling technique which we call Ensemble Surface Slicing (ESS). ESS produces a single image that is useful for determining differences and similarities between surfaces simultaneously from several data sets. We demonstrate the usefulness of ESS on two real-world data sets from our collaborators.

Approach-specific multi-grid anatomical modeling for neurosurgery simulation with public-domain and open-source software.

We present on-going work on multi-resolution sulcal-separable meshing for approach-specific neurosurgery simulation, in conjunction multi-grid and Total Lagrangian Explicit Dynamics finite elements. Conflicting requirements of interactive nonlinear finite elements and small structures lead to a multi-grid framework. Implications for meshing are explicit control over resolution, and prior knowledge of the intended neurosurgical approach and intended path. This information is used to define a subvolume of clinical interest, within some distance of the path and the target pathology. Restricted to this subvolume are a tetrahedralization of finer resolution, the representation of critical tissues, and sulcal separability constraint for all mesh levels.

Segmentation and reconstruction of vascular structures for 3D real-time simulation.

We propose a technique to obtain accurate and smooth surfaces of patient specific vascular structures, using two steps: segmentation and reconstruction. The first step provides accurate and smooth centerlines of the vessels, together with cross section orientations and cross section fitting. The initial centerlines are obtained from a homotopic thinning of the vessels segmented using a level set method. In addition to circle fitting, an iterative scheme fitting ellipses to the cross sections and correcting the centerline positions is proposed, leading to a strong improvement of the cross section orientations and of the location of the centerlines. The second step consists of reconstructing the surface based on this data, by generating a set of topologically preserved quadrilateral patches of branching tubular structures. It improves Felkel's meshing method (Felkel et al., 2004) by: allowing a vessel to have multiple parents and children, reducing undersampling artifacts, and adapting the cross section distribution. Experiments, on phantom and real datasets, show that the proposed technique reaches a good balance in terms of smoothness, number of triangles, and distance error. This technique can be applied in interventional radiology simulations, virtual endoscopy and in reconstruction of smooth and accurate three-dimensional models for use in simulation.

Simulation of brain mass effect with an arbitrary Lagrangian and Eulerian FEM.

Estimation of intracranial stress distribution caused by mass effect is critical to the management of hemorrhagic stroke or brain tumor patients, who may suffer severe secondary brain injury from brain tissue compression. Coupling with physiological parameters that are readily available using MRI, eg, tissue perfusion, a non-invasive, quantitative and regional estimation of intracranial stress distribution could offer a better understanding of brain tissue's reaction under mass effect. A quantitative and sound measurement serving this particular purpose remains elusive due to multiple challenges associated with biomechanical modeling of the brain. One such challenge for the conventional Lagrangian frame based finite element method (LFEM) is that the mesh distortion resulted from the expansion of the mass effects can terminate the simulation prematurely before the desired pressure loading is achieved. In this work, we adopted an arbitrary Lagrangian and Eulerian FEM method (ALEF) with explicit dynamic solutions to simulate the expansion of brain mass effects caused by a pressure loading. This approach consists of three phases: 1) a Lagrangian phase to deform mesh like LFEM, 2) a mesh smoothing phase to reduce mesh distortion, and 3) an Eulerian phase to map the state variables from the old mesh to the smoothed one. In 2D simulations with simulated geometries, this approach is able to model substantially larger deformations compared to LFEM. We further applied this approach to a simulation with 3D real brain geometry to quantify the distribution of von Mises stress within the brain.

Real-time modeling of vascular flow for angiography simulation.

Interventional neuroradiology is a growing field of minimally invasive therapies that includes embolization of aneurysms and arteriovenous malformations, carotid angioplasty and carotid stenting, and acute stroke therapy. Treatment is performed using image-guided instrument navigation through the patient's vasculature and requires intricate combination of visual and tactile coordination. In this paper we present a series of techniques for real-time high-fidelity simulation of angiographic studies. We focus in particular on the computation and visualization of blood flow and blood pressure distribution patterns, mixing of blood and contrast agent, and high-fidelity simulation of fluoroscopic images.

Smooth vasculature reconstruction with circular and elliptic cross sections.

This paper presents a method to segment and reconstruct vascular structure from patient volumetric scan. First, a semi-automatic segmentation phase leads to the vessels centerlines and the estimated circular or elliptic cross section description. Then, the skeleton data are used by the reconstruction phase to generate the three dimensional vascular surface. This structured surface is able to handle interactive visualization, real-time and robust physics-based modeling. The accuracy and consistency of our technique are evaluated on a vascular phantom as well as two clinical data sets. Experiments show that the proposed technique reaches a good balance in terms of mesh smoothness, compactness, and accuracy, where elliptic cross section estimation induces lower error.

New approaches to computer-based interventional neuroradiology training.

For over 20 years, interventional methods have substantially improved the outcomes of patients with cardiovascular disease. However, these procedures require an intricate combination of visual and tactile feedback and extensive training periods. In this paper, a prototype of endovascular therapy training system is presented. A set of core simulation components applicable to most vascular procedures has been designed and integrated into a real-time high-fidelity interventional neuroradiology training system for the prompt treatment of ischemic stroke. We believe it will improve the quality of training and the speed of learning without putting patients at risk.

A segmentation and reconstruction technique for 3D vascular structures.

In the context of stroke therapy simulation, a method for the segmentation and reconstruction of human vasculature is presented and evaluated. Based on CTA scans, semi-automatic tools have been developed to reduce dataset noise, to segment using active contours, to extract the skeleton, to estimate the vessel radii and to reconstruct the associated surface. The robustness and accuracy of our technique are evaluated on a vascular phantom scanned in different orientations. The reconstructed surface is compared to a surface generated by marching cubes followed by decimation and smoothing. Experiments show that the proposed technique reaches a good balance in terms of smoothness, number of triangles, and distance error. The reconstructed surface is suitable for real-time simulation, interactive navigation and visualization.