High-performanceGPU-basedRendering for Real-Time, rigid2D/3D-ImageRegistration and MotionPrediction in RadiationOncology

Jakob Spörk, Christelle Gendrin, Christoph Weber, Michael Figl, Supriyanto Ardjo Pawiro, Hugo Furtado, Christoph Bloch, Helmar Bergmann, Meister Eduard Gröller, Wolfgang Birkfellner
High-performanceGPU-basedRendering for Real-Time, rigid2D/3D-ImageRegistration and MotionPrediction in RadiationOncology
Zeitschrift für Medizinische Physik, (), July 2011.

Information

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.

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BibTeX

@article{Groeller_2011_HP,
  title =      "High-performanceGPU-basedRendering for Real-Time,
               rigid2D/3D-ImageRegistration and MotionPrediction in
               RadiationOncology",
  author =     "Jakob Sp{"o}rk and Christelle Gendrin and Christoph Weber
               and Michael Figl and Supriyanto Ardjo Pawiro and Hugo
               Furtado and Christoph Bloch and Helmar Bergmann and Meister
               Eduard Gr{"o}ller and Wolfgang Birkfellner",
  year =       "2011",
  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.",
  month =      jul,
  journal =    "Zeitschrift f{"u}r Medizinische Physik",
  note =       "availabe online",
  publisher =  "Elsevier B.V.",
  keywords =   "real-time, sparse sampling, DRR, 2D/3D-registration",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2011/Groeller_2011_HP/",
}