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Dynamic volume rendering of the beating heart is an important element in cardiac disease diagnosis and therapy
planning, providing the clinician with insight into the internal cardiac structure and functional behavior. Most
clinical applications tend to focus upon a particular set of organ structures, and in the case of cardiac imaging,
it would be helpful to embed anatomical features into the dynamic volume that are of particular importance to
an intervention. A uniform transfer function (TF), such as is generally employed in volume rendering, cannot
effectively isolate such structures because of the lack of spatial information and the small intensity differences
between adjacent tissues. Explicit segmentation is a powerful way to approach this problem, which usually
yields a single binary mask volume (MV), where a unit value in a voxel within the MV acts as a tag label
representing the anatomical structure of interest (ASOI). These labels are used to determine the TF employed
to adjust the ASOI display. Traditional approaches for rendering such segmented volumetric datasets usually
deliver unsatisfactory results, such as noninteractive rendering speed, low image quality, intermixing artifacts
along the rendered subvolume boundaries, and speckle noise. In this paper, we introduce a new "color coding"
approach, based on the graphics processing unit (GPU) accelerated raycasting algorithm and a pre-integrated
voxel classification method, to address this problem. The mask tag labels derived from segmentation are first
smoothed with a Gaussian filter, and multiple TFs are designed for each of the MVs and the source cardiac
volume respectively, mapping the voxel's intensity to color and opacity at each sampling point along the casting
ray. The resultant values are composited together using a boundary color adjustment technique, which acts as
"coding" the segmented anatomical structure information into the rendered source volume of the beating heart.
Our algorithm produces high image quality in real-time without introducing intermixing artifacts in the rendered
4-dimensional (4D) cardiac volumes.
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