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GPU-accelerated iterative 3D CT reconstruction using exact ray-tracing method for both projection and backprojection

机译:使用投影和反投影的精确光线跟踪方法,GPU加速的迭代3D CT重建

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The model-based iterative reconstruction methods have recently found its popularity in X-ray CT reconstruction due to its ability in providing improved image quality (than analytical methods) especially in low-dose condition where it requires less radiation dose delivering through the patient body. However, the application of iterative methods in practice is still limited due to its expensive computation time. In particular, the iterative methods require time-consuming calculations for repeated projection and backprojection operations. This requirement is more severe in reconstruction from low-dose scan where more iterations are required to regularize the high noise due to low detected counts. While the computational speed of projection and backprojection has been dramatically increased by using GPUs (graphics processing units), efforts to improve the accuracy of modeling a projector-backprojector pair have been hindered by the needs for approximations to maximize the efficiency of the GPU. The unmatched projector-backprojector pairs often used for GPU-accelerated methods also cause additional errors in iterative reconstruction. For low-dose CT reconstruction, the degradation due to these errors becomes more significant as the number of iterations is increased. Despite the appearance of many recent advanced methods to perform projection and backprojection, the ray-tracing method (RTM) is still popular due to its accurate representation of the physics of the Beer's law as well as the ease of use. In this work, we propose a GPU-accelerated RTM. Unlike the previous works that used the RTM in forward projection and the pixel-driven method in backprojection, this work develops a new GPU-accelerated method for a RTM projector-backprojector pair which does not use any approximations for parallelizing the projection and backprojection. Since our method is exact, the results are as accurate as those obtained from the non-accelerated method.
机译:基于模型的迭代重建方法最近在X射线CT重建中广受欢迎,这是因为它具有提供改善的图像质量(比分析方法更好)的能力,尤其是在低剂量条件下,这种情况下需要通过患者体内传递较少的辐射剂量。但是,由于其昂贵的计算时间,因此迭代方法在实践中的应用仍然受到限制。尤其是,迭代方法需要进行重复投影和反投影操作的耗时计算。从低剂量扫描进行重建时,此要求更为严格,在低剂量扫描中,由于检测到的计数较低,因此需要更多的迭代次数来规范高噪声。尽管通过使用GPU(图形处理单元)大大提高了投影和反投影的计算速度,但由于需要进行近似化以最大化GPU效率的要求,阻碍了提高对投影仪-反投影仪对建模的准确性的努力。通常用于GPU加速方法的无与伦比的投影机-背投投影机对在迭代重建中也会引起其他错误。对于低剂量CT重建,由于这些错误导致的降级随着迭代次数的增加而变得更加明显。尽管出现了许多用于执行投影和反投影的高级方法,但由于光线跟踪方法(RTM)可以准确表示比尔定律的物理学原理,并且易于使用,因此仍然很受欢迎。在这项工作中,我们提出了GPU加速的RTM。与以前的将RTM用于正向投影和将像素驱动方法用于反投影的先前作品不同,这项工作为RTM投影机-反投影器对开发了一种新的GPU加速方法,该方法不使用任何近似值来并行化投影和反投影。由于我们的方法是精确的,因此结果与从非加速方法获得的结果一样准确。

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