A Parallel Pipeline Convolution for Perspective Projection in Real-Time Volume Rendering

This paper describes a convolution with a systolic array structure for perspective projection in real-time volume graphics based on the shear-warp method. In the original method, the further the ray proceeds, the more voxels are required to calculate the convolution. The increase in required voxels makes it difficult to implement the method in a VLSI-oriented architecture. We implement a 3D convolution using three serial 1D convolutions along the X, Y, and Z axes, which reduces the number of calculation units from M3 to 3M, where the convolution is calculated for the M3 area. The number of pipelines for the rays is V2 for V3 voxel datasets. If the hardware of a single pipeline can calculate the V rays, then each of the implemented pipelines is assigned to V theoretical pipelines (for V2 rays). The number of hardware pipelines should be much smaller than V theoretical pipelines in actual implementation. We folded the theoretical pipelines and reduced them to a certain number of hardware pipelines. We examined the relation between the folding process and its necessary time delay. The architecture can generate an image of a 2563 voxel dataset (V=256) at 30Hz with four pipelines. In addition, the architecture can be extended easily for 5123 (V=512) and 10243 (V=1024) datasets, with 32 pipelines and 256 pipelines. Our architecture has processing scalability.Engineering and Applied Science

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

The volume in focus: hardwareassisted focus and context effects for volume visualization

In many volume visualization applications there is some region of specific interest where we wish to see fine detail - yet we do not want to lose an impression of the overall picture. In this research we apply the notion of focus and context to texture-based volume rendering. A framework has been developed that enables users to achieve fast volumetric distortion and other effects of practical use. The framework has been implemented through direct programming of the graphics processor and integrated into a volume rendering system. Our driving application is the effective visualization of aneurysms, an important issue in neurosurgery. We have developed and evaluated an easy-to-use system that allows a neurosurgicalteam to explore the nature of cerebral aneurysms, visualizing the aneurysm itself in fine detail while still retaining a view of the surrounding vasculature

Single-image Tomography: 3D Volumes from 2D Cranial X-Rays

As many different 3D volumes could produce the same 2D x-ray image, inverting this process is challenging. We show that recent deep learning-based convolutional neural networks can solve this task. As the main challenge in learning is the sheer amount of data created when extending the 2D image into a 3D volume, we suggest firstly to learn a coarse, fixed-resolution volume which is then fused in a second step with the input x-ray into a high-resolution volume. To train and validate our approach we introduce a new dataset that comprises of close to half a million computer-simulated 2D x-ray images of 3D volumes scanned from 175 mammalian species. Applications of our approach include stereoscopic rendering of legacy x-ray images, re-rendering of x-rays including changes of illumination, view pose or geometry. Our evaluation includes comparison to previous tomography work, previous learning methods using our data, a user study and application to a set of real x-rays

Surface Shape Perception in Volumetric Stereo Displays

Research have shown that specifically designed textures applied on geometrical surfaces can greatly enhance human perception of the shape, orientation and spatial relationships of the surfaces. This is especially so for surfaces in a stereoscopic display environment. However, virtually all practical systems of volumetric rendering use no texture. Previous studies that have looked at this issue used either simple 3D surfaces or terrain surfaces. In this work, we explore the application of textures to more complex surfaces that come from various sources, e.g. an isosurface extracted from a volume dataset. The challenge is to generate uniformly distributed grid like textures on complex surfaces that naturally follow the geometry of the surface. We incorporate the texturing method directly into a fast volume rendering process to enhance the perception of complex surfaces present in a volume dataset. To measure the effectiveness of the texture, we conduct user studies where user is asked to orient a probe to give the estimate of the surface normal at the probe attachment position which will be compared with true surface normal direction