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13.2 Volumetric Image Visualization Methods
Three principle rendering algorithms have been established for volumetric medical image visualization, that is multiplanar reformation (MPR; see Fig. 13.1), surface rendering (SR), and volume rendering (VR). As illustrated in the following sections, the rendering techniques can be categorized as direct and indirect rendering. Direct rendering includes DVR and DSR, while indirect rendering includes ISR. Here, we refer to both DSR and ISR as SR.
13.2.1 Multiplanar Reformation (2D slicing)
MPR is an image processing technique, which extracts 2D slices from a 3D volume using arbitrarily positioned orthogonal or oblique planes [1]. Although it is still a 2D method, it has the advantages of ease of use, high speed, and no information loss. The observer can display a structure of interest in any desired plane within the data set, and 4D MPR can be performed in real time using graphics hardware [2]. 2D multiplanar reformatting can readily complement 3D volume rendering where the 2D slices of MPR can be readily texture-mapped to cut-planes through a 3D volume.
13.2.2 Surface-Based Rendering
SR [3] is a common method of displaying 3D images. ISR can be considered as surface modeling, while DSR is a special case of DVR. ISR requires that the surfaces of relevant structure boundaries within the volume be identified a priori by segmentation and representation as a series of surface tiles using isosurface extracting such as marching cubes [4] or region growing [5], and can be accelerated by taking advantage of graphics processing unit (GPU) and geometry shaders [6, 7]. Such models reduce the amount of data to be displayed by several orders of magnitude, making it easy to manipulate the surfaces interactively with reasonable fidelity. For DSR, the surfaces are rendered directly from the volume without intermediate geometric representations, setting thresholds or using object labels to define a range of voxel intensities to be viewed. Only those voxels within this range, or which have labels, are selected and rendered with DVR. Surface reconstruction can also be improved further by employing GPUs [8].
A fully parallel isosurface extraction algorithm was presented by Zhang et al. [9]. In addition, a high degree of realism can be achieved with lighting models that simulate realistic viewing conditions. Figure 13.2a illustrates an implementation of ISR, while Fig. 13.3b shows the results of DSR for comparison.
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Fig. 13.2 (a) ISR of cardiac structures: myocardium (myo), and the left atrium and aorta (LAA), compared with (b) DSR of an MR cardiac volume and heart phantom
Fig. 13.3 SR applications: (a) image generated with MIP and shaded SR, demonstrating the proximal middle cerebral artery mainstem occlusion (arrow) [12]; (b). SR display of the endoluminal CT colonographic data, showing sessile morphology of the lesion (arrow) [13]
SR is often applied to contrast-enhanced CT data for displaying skeletal and vascular structures, and is also usually used in describing vascular disease and dislocations. In the process of detecting acute ischemic stroke, Schellinger and his colleagues [10] combined MIP with shaded SR to visualize proximal middle cere-
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Fig. 13.4 Volume rendering pipeline and corresponding numerical operations
bral artery mainstem occlusion (Fig. 13.3a). SR has also been exploited to describe polyps within the 3D endoluminal CT colonographic images [11] (Fig. 13.3b).
We note that sometimes it is difficult to justify the accuracy and reliability of the images generated with shaded SR, that is the shiny surfaces might be misleading, causing the relation between image data and brightness in the resultant image becomes more complex, a property which could affect the diagnosis.
13.2.3 Volumetric Rendering
DVR displays the entire 3D dataset by tracing rays through the volume and projecting onto a 2D image, without computing any intermediate geometry representations [14–16]. This algorithm can be further divided into image-space DVR, such as software- [17, 18] and GPU-based raycasting [19], and object-space DVR, such as splatting [20,21], shell rendering [22], TM [23], and cell projection [24]. Shear-warp [25] can be considered as a combination of these two categories. In addition, MIP [26], minimum intensity projection (MinIP), and X-ray projection [27] are also widely used methods for displaying 3D medical images. This chapter now focuses its attentions on DVR, and the datasets discussed here are assumed to be represented on cubic and uniform rectilinear grids, such as are provided by standard 3D medical imaging modalities. Figure 13.4 describes the DVR pipeline with corresponding computations, which are described in detail in the next section that is followed by a discussion of traditional DVR algorithms. Figure 13.5 shows an example of our DVR results applied to an MR cardiac volume.
When compared with SR, the main advantage of DVR is that interior information is retained, and so provides more information about the spatial relationships of different structures [14]. In clinical applications, sensitivity and specificity are the