title: High-performance GPU-based rendering for real-time, rigid 2D/3D-image registration and motion prediction in radiation oncology.
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 radiation oncology 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/3D Registration. In 2D/3D registration, 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-time registration, which should at least provide data on organ position in less than a second, has not come into existence. We present two GPU-based rendering 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-rigid 2D/3D registration and, beyond that, adaptive filtering of motion models in IGRT.
authors.affiliation: Center of Medical Physics and Biomedical Engineering, Medical University of Vienna, Austria.
authors.forename: Jakob
authors.initials: J
authors.lastname: Spoerk
authors.forename: Christelle
authors.initials: C
authors.lastname: Gendrin
authors.forename: Christoph
authors.initials: C
authors.lastname: Weber
authors.forename: Michael
authors.initials: M
authors.lastname: Figl
authors.forename: Supriyanto Ardjo
authors.initials: SA
authors.lastname: Pawiro
authors.forename: Hugo
authors.initials: H
authors.lastname: Furtado
authors.forename: Daniella
authors.initials: D
authors.lastname: Fabri
authors.forename: Christoph
authors.initials: C
authors.lastname: Bloch
authors.forename: Helmar
authors.initials: H
authors.lastname: Bergmann
authors.forename: Eduard
authors.initials: E
authors.lastname: Gröller
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Birkfellner
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003196:Computer Graphics; D054893:Cone-Beam Computed Tomography / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D009369:Neoplasms / Q000532:radiotherapy*; D019047:Phantoms, Imaging; D011832:Radiation Injuries / Q000517:prevention & control; D011856:Radiographic Image Enhancement / Q000379:methods; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D012984:Software