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Fast Simulation of X-ray Projections of Spline-based Surfaces using an Append Buffer

机译:使用Append缓冲区快速模拟X射线表面的X射线投影

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摘要

Many scientists in the field of x-ray imaging rely on the simulation of x-ray images. As the phantom models become more and more realistic, their projection requires high computational effort. Since x-ray images are based on transmission, many standard graphics acceleration algorithms cannot be applied to this task. However, if adapted properly, simulation speed can be increased dramatically using state-of-the-art graphics hardware.A custom graphics pipeline that simulates transmission projections for tomographic reconstruction was implemented based on moving spline surface models. All steps from tessellation of the splines, projection onto the detector, and drawing are implemented in OpenCL. We introduced a special append buffer for increased performance in order to store the intersections with the scene for every ray. Intersections are then sorted and resolved to materials. Lastly, an absorption model is evaluated to yield an absorption value for each projection pixel.Projection of a moving spline structure is fast and accurate. Projections of size 640×480 can be generated within 254 ms. Reconstructions using the projections show errors below 1 HU with a sharp reconstruction kernel. Traditional GPU-based acceleration schemes are not suitable for our reconstruction task. Even in the absence of noise, they result in errors up to 9 HU on average, although projection images appear to be correct under visual examination.Projections generated with our new method are suitable for the validation of novel CT reconstruction algorithms. For complex simulations, such as the evaluation of motion-compensated reconstruction algorithms, this kind of x-ray simulation will reduce the computation time dramatically. Source code is available at
机译:X射线成像领域的许多科学家都依赖于X射线图像的模拟。随着幻影模型变得越来越现实,它们的投影需要大量的计算工作。由于X射线图像基于传输,因此许多标准图形加速算法无法应用于此任务。但是,如果进行适当的调整,则可以使用最新的图形硬件来显着提高仿真速度。基于移动样条曲面模型的自定义图形管线可以模拟用于层析重建的透射投影。从花键的细分,投影到检测器上以及绘制的所有步骤都在OpenCL中实现。我们引入了特殊的追加缓冲区以提高性能,以便为每条光线存储与场景的交集。然后将交叉点分类并分解为材料。最后,评估吸收模型以产生每个投影像素的吸收值。运动样条结构的投影快速而准确。可以在254毫秒内生成640×480大小的投影。使用投影进行的重建显示出一个尖锐的重建内核,误差低于1 HU。传统的基于GPU的加速方案不适合我们的重建任务。即使在没有噪声的情况下,它们也平均会导致高达9 HU的误差,尽管在目测下投影图像看起来是正确的。使用我们的新方法生成的投影也适合验证新型CT重建算法。对于复杂的模拟,例如运动补偿重建算法的评估,这种X射线模拟将大大减少计算时间。源代码位于

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