title: Fast perspective volume ray casting method using GPU-based acceleration techniques for translucency rendering in 3D endoluminal CT colonography.
abstract: Recent advances in graphics processing unit (GPU) have enabled direct volume rendering at interactive rates. However, although perspective volume rendering for opaque isosurface is rapidly performed using conventional GPU-based method, perspective volume rendering for non-opaque volume such as translucency rendering is still slow. In this paper, we propose an efficient GPU-based acceleration technique of fast perspective volume ray casting for translucency rendering in computed tomography (CT) colonography. The empty space searching step is separated from the shading and compositing steps, and they are divided into separate processing passes in the GPU. Using this multi-pass acceleration, empty space leaping is performed exactly at the voxel level rather than at the block level, so that the efficiency of empty space leaping is maximized for colon data set, which has many curved or narrow regions. In addition, the numbers of shading and compositing steps are fixed, and additional empty space leapings between colon walls are performed to increase computational efficiency further near the haustral folds. Experiments were performed to illustrate the efficiency of the proposed scheme compared with the conventional GPU-based method, which has been known to be the fastest algorithm. The experimental results showed that the rendering speed of our method was 7.72fps for translucency rendering of 1024x1024 colonoscopy image, which was about 3.54 times faster than that of the conventional method. Since our method performed the fully optimized empty space leaping for any kind of colon inner shapes, the frame-rate variations of our method were about two times smaller than that of the conventional method to guarantee smooth navigation. The proposed method could be successfully applied to help diagnose colon cancer using translucency rendering in virtual colonoscopy.
authors.affiliation: School of Computer Science and Engineering, Seoul National University, Kwanak-gu, Seoul 151-742, Republic of Korea.
authors.forename: Taek-Hee
authors.initials: TH
authors.lastname: Lee
authors.forename: Jeongjin
authors.initials: J
authors.lastname: Lee
authors.forename: Ho
authors.initials: H
authors.lastname: Lee
authors.forename: Heewon
authors.initials: H
authors.lastname: Kye
authors.forename: Yeong Gil
authors.initials: YG
authors.lastname: Shin
authors.forename: Soo Hong
authors.initials: SH
authors.lastname: Kim
mesh_terms: D003106:Colon / Q000473:pathology; D003110:Colonic Neoplasms / Q000175:diagnosis*; D003113:Colonoscopy / Q000379:methods*; D019295:Computational Biology / Q000295:instrumentation* / Q000379:methods*; D003198:Computer Simulation; D003201:Computers; D003626:Data Display; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional; D015203:Reproducibility of Results; D012984:Software; D013997:Time Factors; D014057:Tomography, X-Ray Computed / Q000295:instrumentation*; D014584:User-Computer Interface