Virtopsy visualisation: Mixed data gradient model for more accurate thin bone visualization in 3D rendering

Abstract

Conventional 3D rendering methods of computed tomography (CT) as well as post-mortem data CT (PMCT) sometimes do not seem to be authentic enough, especially for relatively thin bones. This can be a problem when imaging intact anatomy and considering fractures of the facial or temporal bones, where defects or holes may be visualized instead of thin bone structures. The technical aspect of this is that all currently used visualization methods (volume rendering, cinematic rendering and particle tracing, shaded surfaces and iso-surfaces) are defined by a CT-density threshold, whereas the user at least implicitly expects the bone to have a certain minimum density CT. However, some bone regions, typically those with relatively thin bone, do not meet these expectations, and lowering the threshold for visualization then results in all sorts of non-bone tissue being seen in the rendered images. To provide a more authentic PMCT visualization of bone, we identified a mixed data gradient model that improves the data from CT by increasing the CT density of low-density bone regions (but not of non-bone tissues). That delivers more satisfactory results for otherwise unmodified volume rendering. As pre-processing before 3D rendering, both hard and soft kernel data are used to obtain a 3D density map, a grayscale co-occurrence matrix is determined using a $3\times 3\times 3$ kernel as the 3D gradient map, and these are then combined to obtain the final gradient model for mixed data.