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High-quality anatomical structure enhancement for cardiac image dynamic volume rendering

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.