Interactive GPU-based maximum intensity projection of large medical data sets using visibility culling based on the initial occluder and the visible block classification
Maximum intensity projection (MIP) is an important visualization method that has been widely used for the diagnosis of enhanced vessels or bones by rotating or zooming MIP images. With the rapid spread of multidetector-row computed tomography (MDCT) scanners, MDCT scans of a patient generate a large data set. However, previous acceleration methods for MIP rendering of such a data set failed to generate MIP images at interactive rates. In this paper, we propose novel culling methods in both object and image space for interactive MIP rendering of large medical data sets. In object space, for the visibility test of a block, we propose the initial occluder resulting from a preceding image to utilize temporal coherence and increase the block culling ratio a lot. In addition, we propose the hole filling method using the mesh generation and rendering to improve the culling performance during the generation of the initial occluder. In image space, we find out that there is a trade-off between the block culling ratio in object space and the culling efficiency in image space. In this paper, we classify the visible blocks into two types by their visibility. And we propose a balanced culling method by applying a different culling algorithm in image space for each type to utilize the trade-off and improve the rendering speed. Experimental results on twenty CT data sets showed that our method achieved 3.85 times speed up in average without any loss of image quality comparing with conventional bricking method. Using our visibility culling method, we achieved interactive GPU-based MIP rendering of large medical data sets.
visibility culling initial occluder block classification maximum intensity projection (mip) graphics processing unit (gpu) large volume data set