Volume rendering

Volume Ray Casting. Crocodile mummy provided by the Phoebe A. Hearst Museum of Anthropology, UC Berkeley. CT data was acquired by Dr. Rebecca Fahrig, Department of Radiology, Stanford University, using a Siemens SOMATOM Definition, Siemens Healthcare. The image was rendered by Fovia's High Definition Volume Rendering® engine

The technique of volume ray casting can be derived directly from the rendering equation. It provides results of very high quality, usually considered to provide the best image quality. Volume ray casting is classified as image based volume rendering technique, as the computation emanates from the output image, not the input volume data as is the case with object based techniques. In this technique, a ray is generated for each desired image pixel. Using a simple camera model, the ray starts at the center of projection of the camera (usually the eye point) and passes through the image pixel on the imaginary image plane floating in between the camera and the volume to be rendered. The ray is clipped by the boundaries of the volume in order to save time. Then the ray is sampled at regular or adaptive intervals throughout the volume. The data is interpolated at each sample point, the transfer function applied to form an RGBA sample, the sample is composited onto the accumulated RGBA of the ray, and the process repeated until the ray exits the volume. The RGBA color is converted to an RGB color and deposited in the corresponding image pixel. The process is repeated for every pixel on the screen to form the completed image.

This is a technique which trades quality for speed. Here, every volume element is splatted, as Lee Westover said, like a snow ball, on to the viewing surface in back to front order. These splats are rendered as disks whose properties (color and transparency) vary diametrically in normal (Gaussian) manner. Flat disks and those with other kinds of property distribution are also used depending on the application.[6][7]

Example of a mouse skull (CT) rendering using the shear warp algorithm

The shear warp approach to volume rendering was developed by Cameron and Undrill, popularized by Philippe Lacroute and Marc Levoy.[8] In this technique, the viewing transformation is transformed such that the nearest face of the volume becomes axis aligned with an off-screen image data buffer with a fixed scale of voxels to pixels. The volume is then rendered into this buffer using the far more favorable memory alignment and fixed scaling and blending factors. Once all slices of the volume have been rendered, the buffer is then warped into the desired orientation and scaled in the displayed image.

This technique is relatively fast in software at the cost of less accurate sampling and potentially worse image quality compared to ray casting. There is memory overhead for storing multiple copies of the volume, for the ability to have near axis aligned volumes. This overhead can be mitigated using run length encoding.

A volume rendered cadaver head using view-aligned texture mapping and diffuse reflection

Many 3D graphics systems use texture mapping to apply images, or textures, to geometric objects. Commodity PC graphics cards are fast at texturing and can efficiently render slices of a 3D volume, with real time interaction capabilities. Workstation GPUs are even faster, and are the basis for much of the production volume visualization used in medical imaging, oil and gas, and other markets (2007). In earlier years, dedicated 3D texture mapping systems were used on graphics systems such as Silicon Graphics InfiniteReality, HP Visualize FX graphics accelerator, and others. This technique was first described by Bill Hibbard and Dave Santek.[9]

These slices can either be aligned with the volume and rendered at an angle to the viewer, or aligned with the viewing plane and sampled from unaligned slices through the volume. Graphics hardware support for 3D textures is needed for the second technique.

Volume aligned texturing produces images of reasonable quality, though there is often a noticeable transition when the volume is rotated.

Often, a volume rendering system will have a system for identifying regions of the volume containing no visible material. This information can be used to avoid rendering these transparent regions.[13]

This is a technique used when the volume is rendered in front to back order. For a ray through a pixel, once sufficient dense material has been encountered, further samples will make no significant contribution to the pixel and so may be neglected.

The use of hierarchical structures such as octree and BSP-tree could be very helpful for both compression of volume data and speed optimization of volumetric ray casting process.

Volume segmentation include automatic bone removal, such as used in the right image in this CT angiography.

Volume segmentation of a 3D-rendered CT scan of the thorax: The anterior thoracic wall, the airways and the pulmonary vessels anterior to the root of the lung have been digitally removed in order to visualize thoracic contents:
- blue: pulmonary arteries
- red: pulmonary veins (and also the abdominal wall)
- yellow: the mediastinum
- violet: the diaphragm

Image segmentation is a manual or automatic procedure that can be used to section out large portions of the volume that one considers uninteresting before rendering, the amount of calculations that have to be made by ray casting or texture blending can be significantly reduced. This reduction can be as much as from O(n) to O(log n) for n sequentially indexed voxels. Volume segmentation also has significant performance benefits for other ray tracing algorithms. Volume segmentation can subsequently be used to highlight structures of interest.

By representing less interesting regions of the volume in a coarser resolution, the data input overhead can be reduced. On closer observation, the data in these regions can be populated either by reading from memory or disk, or by interpolation. The coarser resolution volume is resampled to a smaller size in the same way as a 2D mipmap image is created from the original. These smaller volume are also used by themselves while rotating the volume to a new orientation.

Pre-integrated volume rendering[14] is a method that can reduce sampling artifacts by pre-computing much of the required data. It is especially useful in hardware-accelerated applications[15][16] because it improves quality without a large performance impact. Unlike most other optimizations, this does not skip voxels. Rather it reduces the number of samples needed to accurately display a region of voxels. The idea is to render the intervals between the samples instead of the samples themselves. This technique captures rapidly changing material, for example the transition from muscle to bone with much less computation.

Image-based meshing is the automated process of creating computer models from 3D image data (such as MRI, CT, Industrial CT or microtomography) for computational analysis and design, e.g. CAD, CFD, and FEA.

For a complete display view, only one voxel per pixel (the front one) is required to be shown (although more can be used for smoothing the image), if animation is needed, the front voxels to be shown can be cached and their location relative to the camera can be recalculated as it moves. Where display voxels become too far apart to cover all the pixels, new front voxels can be found by ray casting or similar, and where two voxels are in one pixel, the front one can be kept.