[Back]


Contributions to Books:

J. Spörk, E. Gröller et al.:
"High-performanceGPU-basedRendering for Real-Time, rigid2D/3D-ImageRegistration and MotionPrediction in RadiationOncology";
in: "Zeitschrift für Medizinische Physik", Elsevier B.V., 2011.



English abstract:
A common problem in image-guided radiation therapy (IGRT) of lung cancer as well as other malignant diseases is the compensation of periodic and aperiodic motion during dose delivery. Modern systems for image-guided radiationoncology allow for the acquisition of cone-beam computed tomography data in the treatment room as well as the acquisition of planar radiographs during the treatment. A mid-term research goal is the compensation of tumor target volume motion by 2D/3Dregistration. In 2D/3Dregistration, spatial information on organ location is derived by an iterative comparison of perspective volume renderings, so-called digitally rendered radiographs (DRR) from computed tomography volume data, and planar reference x-rays. Currently, this rendering process is very time consuming, and real-timeregistration, which should at least provide data on organ position in less than a second, has not come into existence. We present two GPU-basedrendering algorithms which generate a DRR of 512 512 pixels size from a CT dataset of 53 MB size at a pace of almost 100 Hz. This rendering rate is feasible by applying a number of algorithmic simplifications which range from alternative volume-driven rendering approaches - namely so-called wobbled splatting - to sub-sampling of the DRR-image by means of specialized raycasting techniques. Furthermore, general purpose graphics processing unit (GPGPU) programming paradigms were consequently utilized. Rendering quality and performance as well as the influence on the quality and performance of the overall registration process were measured and analyzed in detail. The results show that both methods are competitive and pave the way for fast motion compensation by rigid and possibly even non-rigid2D/3Dregistration and, beyond that, adaptive filtering of motion models in IGRT.

Created from the Publication Database of the Vienna University of Technology.