In this work, we describe the CRIMSON (CardiovasculaR Integrated Modelling and SimulatiON) software environment. CRIMSON provides a powerful, customizable and user-friendly system for performing three-dimensional and reduced-order computational haemodynamics studies via a pipeline which involves: 1) segmenting vascular structures from medical images; 2) constructing analytic arterial and venous geometric models; 3) performing finite element mesh generation; 4) designing, and 5) applying boundary conditions; 6) running incompressible Navier-Stokes simulations of blood flow with fluid-structure interaction capabilities; and 7) post-processing and visualizing the results, including velocity, pressure and wall shear stress fields. A key aim of CRIMSON is to create a software environment that makes powerful computational haemodynamics tools accessible to a wide audience, including clinicians and students, both within our research laboratories and throughout the community. The overall philosophy is to leverage best-in-class open source standards for medical image processing, parallel flow computation, geometric solid modelling, data assimilation, and mesh generation. It is actively used by researchers in Europe, North and South America, Asia, and Australia. It has been applied to numerous clinical problems; we illustrate applications of CRIMSON to real-world problems using examples ranging from pre-operative surgical planning to medical device design optimization.

Abstract We propose a technique to build the irradiance cache for isotropic scattering simultaneously with Monte Carlo progressive direct volume rendering on a single GPU, which allows us to achieve up to four times increased convergence rate for complex scenes with arbitrary sources of light. We use three procedures that run concurrently on a single GPU. The first is the main rendering procedure. The second procedure computes new cache entries, and the third one corrects the errors that may arise after creation of new cache entries. We propose two distinct approaches to allow massive parallelism of cache entry creation. In addition, we show a novel extrapolation approach which outputs high quality irradiance approximations and a suitable prioritization scheme to increase the convergence rate by dedicating more computational power to more complex rendering areas.

Analysis of multivariate data is of great importance in many scientific disciplines. However, visualization of 3D spatially-fixed multivariate volumetric data is a very challenging task. In this paper we present a method that allows simultaneous real-time visualization of multivariate data. We redistribute the opacity within a voxel to improve the readability of the color defined by a regular transfer function, and to maintain the see-through capabilities of volume rendering. We use predictable procedural noise--random-phase Gabor noise--to generate a high-frequency redistribution pattern and construct an opacity mapping function, which allows to partition the available space among the displayed data attributes. This mapping function is appropriately filtered to avoid aliasing, while maintaining transparent regions. We show the usefulness of our approach on various data sets and with different example applications. Furthermore, we evaluate our method by comparing it to other visualization techniques in a controlled user study. Overall, the results of our study indicate that users are much more accurate in determining exact data values with our novel 3D volume visualization method. Significantly lower error rates for reading data values and high subjective ranking of our method imply that it has a high chance of being adopted for the purpose of visualization of multivariate 3D data.

Abstract Simultaneous visualization of multiple continuous data attributes in a single visualization is a task that is important for many application areas. Unsurprisingly, many methods have been proposed to solve this task. However, the behavior of such methods during the exploration stage, when the user tries to understand the data with panning and zooming, has not been given much attention. In this paper, we propose a method that uses procedural texture synthesis to create zoom‐independent visualizations of three scalar data attributes. The method is based on random‐phase Gabor noise, whose frequency is adapted for the visualization of the first data attribute. We ensure that the resulting texture frequency lies in the range that is perceived well by the human visual system at any zoom level. To enhance the perception of this attribute, we also apply a specially constructed transfer function that is based on statistical properties of the noise. Additionally, the transfer function is constructed in a way that it does not introduce any aliasing to the texture. We map the second attribute to the texture orientation. The third attribute is color coded and combined with the texture by modifying the value component of the HSV color model. The necessary contrast needed for texture and color perception was determined in a user study. In addition, we conducted a second user study that shows significant advantages of our method over current methods with similar goals. We believe that our method is an important step towards creating methods that not only succeed in visualizing multiple data attributes, but also adapt to the behavior of the user during the data exploration stage.

A common challenge in interactive image generation is maintaining high interactivity of the applications that use computationally demanding rendering algorithms. This is usually achieved by sacrificing some of the image quality in order to decrease the rendering time. Most of such algorithms achieve interactive frame rates while trying to preserve as much image quality as possible by applying the reduction steps non-uniformly. However, high-end rendering systems, such as those presented by [Parker et al. 2010], aim to generate highly realistic images of very complex scenes. In such systems ordinary sampling approaches often give visually unacceptable results. In order to allow to optimize the ratio between the sampling rate of the scene and its resulting perceptual quality, we present a new sampling strategy which uses the information about object features that are known to support the comprehension of 3D shape [Cole et al. 2009]. We control the sampling density by exploiting line extraction techniques commonly used in Non-Photorealistic Renderings.

In modern clinical practice, planning access paths to volumetric target structures remains one of the most important and most complex tasks, and a physician's insufficient experience in this can lead to severe complications or even the death of the patient. In this paper, we present a method for safety evaluation and the visualization of access paths to assist physicians during preoperative planning. As a metaphor for our method, we employ a well-known, and thus intuitively perceivable, natural phenomenon that is usually called crepuscular rays. Using this metaphor, we propose several ways to compute the safety of paths from the region of interest to all tumor voxels and show how this information can be visualized in real-time using a multi-volume rendering system. Furthermore, we show how to estimate the extent of connected safe areas to improve common medical 2D multi-planar reconstruction (MPR) views. We evaluate our method by means of expert interviews, an online survey, and a retrospective evaluation of 19 real abdominal radio-frequency ablation (RFA) interventions, with expert decisions serving as a gold standard. The evaluation results show clear evidence that our method can be successfully applied in clinical practice without introducing substantial overhead work for the acting personnel. Finally, we show that our method is not limited to medical applications and that it can also be useful in other fields.