PURPOSE: Simulated 2D X-ray images called digitally reconstructed radiographs (DRRs) have important applications within medical image registration frameworks where they are compared with reference X-rays or used in implementations of digital tomosynthesis (DTS). However, rendering DRRs from a CT volume is computationally demanding and relatively slow using the conventional ray-casting algorithm. Image-guided radiation therapy systems using DTS to verify target location require a large number DRRs to be precomputed since there is insufficient time within the automatic image registration procedure to generate DRRs and search for an optimal pose. METHOD: DRRs were rendered from octree-compressed CT data. Previous work showed that octree-compressed volumes rendered by conventional ray casting deliver a registration with acceptable clinical accuracy, but efficiently rendering the irregular grid of an octree data structure is a challenge for conventional ray casting. We address this by using vertex and fragment shaders of modern graphics processing units (GPUs) to directly project internal spaces of the octree, represented by textured particle sprites, onto the view plane. The texture is procedurally generated and depends on the CT pose. RESULTS: The performance of this new algorithm was found to be 4 times faster than that of a ray-casting algorithm implemented using NVIDIA™Compute Unified Device Architecture (CUDA™) on an equivalent GPU (~95 % octree compression). Rendering artifacts are apparent (consistent with other splatting algorithm), but image quality tends to improve with compression and fewer particles are needed. A peak signal-to-noise ratio analysis confirmed that the images rendered from compressed volumes were of marginally better quality to those rendered using Gaussian footprints. CONCLUSIONS: Using octree-encoded DRRs within a 2D/3D registration framework indicated the approach may be useful in accelerating automatic image registration.
PURPOSE: Simulated 2D X-ray images called digitally reconstructed radiographs (DRRs) have important applications within medical image registration frameworks where they are compared with reference X-rays or used in implementations of digital tomosynthesis (DTS). However, rendering DRRs from a CT volume is computationally demanding and relatively slow using the conventional ray-casting algorithm. Image-guided radiation therapy systems using DTS to verify target location require a large number DRRs to be precomputed since there is insufficient time within the automatic image registration procedure to generate DRRs and search for an optimal pose. METHOD: DRRs were rendered from octree-compressed CT data. Previous work showed that octree-compressed volumes rendered by conventional ray casting deliver a registration with acceptable clinical accuracy, but efficiently rendering the irregular grid of an octree data structure is a challenge for conventional ray casting. We address this by using vertex and fragment shaders of modern graphics processing units (GPUs) to directly project internal spaces of the octree, represented by textured particle sprites, onto the view plane. The texture is procedurally generated and depends on the CT pose. RESULTS: The performance of this new algorithm was found to be 4 times faster than that of a ray-casting algorithm implemented using NVIDIA™Compute Unified Device Architecture (CUDA™) on an equivalent GPU (~95 % octree compression). Rendering artifacts are apparent (consistent with other splatting algorithm), but image quality tends to improve with compression and fewer particles are needed. A peak signal-to-noise ratio analysis confirmed that the images rendered from compressed volumes were of marginally better quality to those rendered using Gaussian footprints. CONCLUSIONS: Using octree-encoded DRRs within a 2D/3D registration framework indicated the approach may be useful in accelerating automatic image registration.
Authors: Wolfgang Birkfellner; Rudolf Seemann; Michael Figl; Johann Hummel; Christopher Ede; Peter Homolka; Xinhui Yang; Peter Niederer; Helmar Bergmann Journal: Phys Med Biol Date: 2005-04-13 Impact factor: 3.609
Authors: Guoyan Zheng; Sebastian Gollmer; Steffen Schumann; Xiao Dong; Thomas Feilkas; Miguel A González Ballester Journal: Med Image Anal Date: 2008-12-24 Impact factor: 8.545
Authors: Yoshito Otake; Adam S Wang; J Webster Stayman; Ali Uneri; Gerhard Kleinszig; Sebastian Vogt; A Jay Khanna; Ziya L Gokaslan; Jeffrey H Siewerdsen Journal: Phys Med Biol Date: 2013-11-18 Impact factor: 3.609