The following text is largely excerpted from the article: Three-dimensional Volume Rendering of Spiral CT Data: Theory and Method. Calhoun PS, Kuszyk BS, David G. Heath DG, et al. (Radiographics. 1999;19:745-764.)
The rapid development of spiral (helical) computed tomography (CT) has resulted in exciting new applications for CT.
Three-dimensional images integrate a series of axial CT sections into a form that is often easier to interpret than the sections themselves and can be made to appear similar to other, more familiar images such as catheter angiograms. The most widely used 3D imaging techniques to date have been shaded surface display (SSD) and maximum intensity projection (MIP). Volume rendering has existed since the mid-1980s but has not been widely available commercially until recently. All 3D rendering techniques represent a 3D volume of data in one or more two-dimensional (2D) planes, conveying the spatial relationships inherent in the data with use of visual depth cues. To understand how these techniques work, it may be helpful to think of the volume of data as a cube floating within a computer monitor. The data are organized into a 3D matrix of volume elements (voxels). The screen of the computer monitor is a 2D surface composed of discrete picture elements (pixels). Presenting what is stored in memory (ie, floating within the monitor) on a 2D screen is a challenge, but it is the very problem that 3D reconstruction software has creatively solved. Each 3D rendering technique relies on mathematic formulas to determine for each pixel what portion of the data in memory should be displayed on the screen and how that portion should be weighted to best represent spatial relationships. Voxel selection is usually accomplished by projecting lines (rays) through the data set that correspond to the pixel matrix of the desired 2D image. Differences in the images produced with various 3D rendering techniques are the result of variations in how voxels are selected and weighted.
Shaded Surface Display
SSD, also known as surface rendering, was the first 3D rendering technique applied to medical data sets. Its early development in the 1970s was a logical extension of new computer graphics and image-processing techniques and innovations in data segmentation (ie, division of a volume into multiple areas or objects [primitives]) and display. Since that time, the medical and computing communities have worked together to develop new applications of and refinements to existing 3D imaging technologies.
SSD is a process in which apparent surfaces are determined within the volume of data and an image representing the derived surfaces is displayed. Much of the research in this area has focused on how surfaces are determined. Results of this research have also been used effectively in volume rendering for automated segmentation. Simple thresholding is a commonly used technique designed to segment structures of interest for surface rendering. In this technique, each voxel intensity within the data set is determined to be within some user-specified range of attenuation values (eg, bone attenuation). The fidelity of the resulting images to actual anatomy depends in part on the value range selected. More advanced surface generation techniques such as "marching cubes" use simple thresholding to select voxels but also use voxel values to generate surfaces that are placed and oriented more accurately.
Surface contours are typically modeled as a number of overlapping polygons derived from the boundary of the selected region of interest. A virtual light source is computed for each polygon, and the object is displayed with the resulting surface shading. Multiple overlapping surfaces can be displayed on a single image with the additional implementation of partial opacities. Surface rendering is widely available in commercial CT image processing packages and is used clinically.