Adaptive transfer functions: improved multiresolution visualization of medical models

The final publication is available at Springer via http://dx.doi.org/10.1007/s00371-016-1253-9Medical datasets are continuously increasing in size. Although larger models may be available for certain research purposes, in the common clinical practice the models are usually of up to 512x512x2000 voxels. These resolutions exceed the capabilities of conventional GPUs, the ones usually found in the medical doctors’ desktop PCs. Commercial solutions typically reduce the data by downsampling the dataset iteratively until it fits the available target specifications. The data loss reduces the visualization quality and this is not commonly compensated with other actions that might alleviate its effects. In this paper, we propose adaptive transfer functions, an algorithm that improves the transfer function in downsampled multiresolution models so that the quality of renderings is highly improved. The technique is simple and lightweight, and it is suitable, not only to visualize huge models that would not fit in a GPU, but also to render not-so-large models in mobile GPUs, which are less capable than their desktop counterparts. Moreover, it can also be used to accelerate rendering frame rates using lower levels of the multiresolution hierarchy while still maintaining high-quality results in a focus and context approach. We also show an evaluation of these results based on perceptual metrics.Peer ReviewedPostprint (author's final draft

Visualizing the inner structure of N-body data

N-body simulations produce a large amount of data that must be visualized in order to extract interesting features. This data consists of thousands of particles, each with a number of properties such as velocity, mass, and acceleration, that are persisted across hundreds of time steps. Rendering these points using traditional means quickly leads to a image that is satu- rated because the number of particles is greater than the number of pixels available. Volume rendering, and particularly splatting, seeks to remedy this and allows one to see inside the volume and discern the inner structure. The definition of inner structure itself can vary and is usually determined using one of the observable properties of the particles

Time-varying volume visualization

Volume rendering is a very active research field in Computer Graphics because of its wide range of applications in various sciences, from medicine to flow mechanics. In this report, we survey a state-of-the-art on time-varying volume rendering. We state several basic concepts and then we establish several criteria to classify the studied works: IVR versus DVR, 4D versus 3D+time, compression techniques, involved architectures, use of parallelism and image-space versus object-space coherence. We also address other related problems as transfer functions and 2D cross-sections computation of time-varying volume data. All the papers reviewed are classified into several tables based on the mentioned classification and, finally, several conclusions are presented.Preprin

Volumetric real-time particle-based representation of large unstructured tetrahedral polygon meshes

In this paper we propose a particle-based volume rendering approach for unstructured, three-dimensional, tetrahedral polygon meshes. We stochastically generate millions of particles per second and project them on the screen in real-time. In contrast to previous rendering techniques of tetrahedral volume meshes, our method does not need a prior depth sorting of geometry. Instead, the rendered image is generated by choosing particles closest to the camera. Furthermore, we use spatial superimposing. Each pixel is constructed from multiple subpixels. This approach not only increases projection accuracy, but allows also a combination of subpixels into one superpixel that creates the well-known translucency effect of volume rendering. We show that our method is fast enough for the visualization of unstructured three-dimensional grids with hard real-time constraints and that it scales well for a high number of particles

Volumetric Isosurface Rendering with Deep Learning-Based Super-Resolution

Rendering an accurate image of an isosurface in a volumetric field typically requires large numbers of data samples. Reducing the number of required samples lies at the core of research in volume rendering. With the advent of deep learning networks, a number of architectures have been proposed recently to infer missing samples in multi-dimensional fields, for applications such as image super-resolution and scan completion. In this paper, we investigate the use of such architectures for learning the upscaling of a low-resolution sampling of an isosurface to a higher resolution, with high fidelity reconstruction of spatial detail and shading. We introduce a fully convolutional neural network, to learn a latent representation generating a smooth, edge-aware normal field and ambient occlusions from a low-resolution normal and depth field. By adding a frame-to-frame motion loss into the learning stage, the upscaling can consider temporal variations and achieves improved frame-to-frame coherence. We demonstrate the quality of the network for isosurfaces which were never seen during training, and discuss remote and in-situ visualization as well as focus+context visualization as potential application

Beyond XSPEC: Towards Highly Configurable Analysis

We present a quantitative comparison between software features of the defacto standard X-ray spectral analysis tool, XSPEC, and ISIS, the Interactive Spectral Interpretation System. Our emphasis is on customized analysis, with ISIS offered as a strong example of configurable software. While noting that XSPEC has been of immense value to astronomers, and that its scientific core is moderately extensible--most commonly via the inclusion of user contributed "local models"--we identify a series of limitations with its use beyond conventional spectral modeling. We argue that from the viewpoint of the astronomical user, the XSPEC internal structure presents a Black Box Problem, with many of its important features hidden from the top-level interface, thus discouraging user customization. Drawing from examples in custom modeling, numerical analysis, parallel computation, visualization, data management, and automated code generation, we show how a numerically scriptable, modular, and extensible analysis platform such as ISIS facilitates many forms of advanced astrophysical inquiry.Comment: Accepted by PASP, for July 2008 (15 pages

Visualizing the inner structure of n-body data using skeletonization

N-body simulations solve the n-body problem numerically and determine the trajectories of the n point masses. The result of these calculations is a huge amount of data detailing the positions and other properties of each body such as mass and velocity. To effectively draw conclusions from these data, one must employ scientific visualization to create images and movies that illustrate the structure of the data. We show that the computer animation technique of skeletonization can be applied to the volume data produced by n-body simulations in order to visualize the inner structure of the data. This novel application is compared to traditional rendering methods in terms of its ability to show this structure

NONINVASIVE NEAR-INFRARED DIFFUSE OPTICAL MONITORING OF CEREBRAL HEMODYNAMICS AND AUTOREGULATION

Many cerebral diseases are associated with abnormal cerebral hemodynamics and impaired cerebral autoregulation (CA). CA is a mechanism to maintain cerebral blood flow (CBF) stable when mean arterial pressure (MAP) fluctuates. Evaluating these abnormalities requires direct measurements of cerebral hemodynamics and MAP. Several near-infrared diffuse optical instruments have been developed in our laboratory for hemodynamic measurements including near-infrared spectroscopy (NIRS), diffuse correlation spectroscopy (DCS), hybrid NIRS/DCS, and dual-wavelength DCS flow-oximeter. We utilized these noninvasive technologies to quantify CBF and cerebral oxygenation in different populations under different physiological conditions/manipulations. A commercial finger plethysmograph was used to continuously monitor MAP. For investigating the impact of obstructive sleep apnea (OSA) on cerebral hemodynamics and CA, a portable DCS device was used to monitor relative changes of CBF (rCBF) during bilateral thigh cuff occlusion. Compared to healthy controls, smaller reductions in rCBF and MAP following cuff deflation were observed in patients with OSA, which might result from the impaired vasodilation. However, dynamic CAs quantified in time-domain (defined by rCBF drop/MAP drop) were not significantly different between the two groups. We also evaluated dynamic CA in frequency-domain, i.e., to quantify the phase shifts of low frequency oscillations (LFOs) at 0.1 Hz between cerebral hemodynamics and MAP under 3 different physiological conditions (i.e., supine resting, head-up tilt (HUT), paced breathing). To capture dynamic LFOs, a hybrid NIRS/DCS device was upgraded to achieve faster sampling rate and better signal-to-noise. We determined the best hemodynamic parameters (i.e., CBF, oxygenated and total hemoglobin concentrations) among the measured variables and optimal physiological condition (HUT) for detecting LFOs in healthy subjects. Finally, a novel dual-wavelength DCS flow-oximeter was developed to monitor cerebral hemodynamics during HUT-induced vasovagal presyncope (VVS) in healthy subjects. rCBF was found to have the best sensitivity for the assessment of VVS among the measured variables and was likely the final trigger of VVS. A threshold of ~50% rCBF decline was observed which can completely separate subjects with or without presyncope, suggesting its potential role for predicting VVS. With further development and applications, NIRS/DCS techniques are expected to have significant impacts on the evaluation of cerebral hemodynamics and autoregulation