PURPOSE: The generation of digitally reconstructed radiographs (DRRs) is the most time consuming step on the CPU in intensity based two-dimensional x-ray to three-dimensional (CT or 3D rotational x-ray) medical image registration, which has application in several image guided interventions. This work presents optimized DRR rendering on graphical processor units (GPUs) and compares performance achievable on four commercially available devices. METHODS: A ray-cast based DRR rendering was implemented for a 512 × 512 × 72 CT volume. The block size parameter was optimized for four different GPUs for a region of interest (ROI) of 400 × 225 pixels with different sampling ratios (1.1%-9.1% and 100%). Performance was statistically evaluated and compared for the four GPUs. The method and the block size dependence were validated on the latest GPU for several parameter settings with a public gold standard dataset (512 × 512 × 825 CT) for registration purposes. RESULTS: Depending on the GPU, the full ROI is rendered in 2.7-5.2 ms. If sampling ratio of 1.1%-9.1% is applied, execution time is in the range of 0.3-7.3 ms. On all GPUs, the mean of the execution time increased linearly with respect to the number of pixels if sampling was used. CONCLUSIONS: The presented results outperform other results from the literature. This indicates that automatic 2D to 3D registration, which typically requires a couple of hundred DRR renderings to converge, can be performed quasi on-line, in less than a second or depending on the application and hardware in less than a couple of seconds. Accordingly, a whole new field of applications is opened for image guided interventions, where the registration is continuously performed to match the real-time x-ray.
PURPOSE: The generation of digitally reconstructed radiographs (DRRs) is the most time consuming step on the CPU in intensity based two-dimensional x-ray to three-dimensional (CT or 3D rotational x-ray) medical image registration, which has application in several image guided interventions. This work presents optimized DRR rendering on graphical processor units (GPUs) and compares performance achievable on four commercially available devices. METHODS: A ray-cast based DRR rendering was implemented for a 512 × 512 × 72 CT volume. The block size parameter was optimized for four different GPUs for a region of interest (ROI) of 400 × 225 pixels with different sampling ratios (1.1%-9.1% and 100%). Performance was statistically evaluated and compared for the four GPUs. The method and the block size dependence were validated on the latest GPU for several parameter settings with a public gold standard dataset (512 × 512 × 825 CT) for registration purposes. RESULTS: Depending on the GPU, the full ROI is rendered in 2.7-5.2 ms. If sampling ratio of 1.1%-9.1% is applied, execution time is in the range of 0.3-7.3 ms. On all GPUs, the mean of the execution time increased linearly with respect to the number of pixels if sampling was used. CONCLUSIONS: The presented results outperform other results from the literature. This indicates that automatic 2D to 3D registration, which typically requires a couple of hundred DRR renderings to converge, can be performed quasi on-line, in less than a second or depending on the application and hardware in less than a couple of seconds. Accordingly, a whole new field of applications is opened for image guided interventions, where the registration is continuously performed to match the real-time x-ray.
Authors: Yoshito Otake; Adam S Wang; Ali Uneri; Gerhard Kleinszig; Sebastian Vogt; Nafi Aygun; Sheng-fu L Lo; Jean-Paul Wolinsky; Ziya L Gokaslan; Jeffrey H Siewerdsen Journal: Phys Med Biol Date: 2015-03-07 Impact factor: 3.609
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