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Multi-GPU Acceleration of Branchless Distance Driven Projection and Backprojection for Clinical Helical CT

机译：临床螺旋CT的无分支距离驱动投影和反投影的多GPU加速

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Model-based image reconstruction (MBIR) techniques have the potential to generate high quality images from noisy measurements and a small number of projections which can reduce the x-ray dose in patients. These MBIR techniques rely on projection and backprojection to refine an image estimate. One of the widely used projectors for these modern MBIR based technique is called branchless distance driven (DD) projection and backprojection. While this method produces superior quality images, the computational cost of iterative updates keeps it from being ubiquitous in clinical applications. In this paper, we provide several new parallelization ideas for concurrent execution of the DD projectors in multi-GPU systems using CUDA programming tools. We have introduced some novel schemes for dividing the projection data and image voxels over multiple GPUs to avoid runtime overhead and inter-device synchronization issues. We have also reduced the complexity of overlap calculation of the algorithm by eliminating the common projection plane and directly projecting the detector boundaries onto image voxel boundaries. To reduce the time required for calculating the overlap between the detector edges and image voxel boundaries, we have proposed a pre-accumulation technique to accumulate image intensities in perpendicular 2D image slabs (from a 3D image) before projection and after backprojection to ensure our DD kernels run faster in parallel GPU threads. For the implementation of our iterative MBIR technique we use a parallel multi-GPU version of the alternating minimization (AM) algorithm with penalized likelihood update. The time performance using our proposed reconstruction method with Siemens Sensation 16 patient scan data shows an average of 24 times speedup using a single TITAN X GPU and 74 times speedup using 3 TITAN X GPUs in parallel for combined projection and backprojection. (C) 2017 Society for Imaging Science and Technology.
机译：基于模型的图像重建（MBIR）技术具有从嘈杂的测量结果和少量投影中生成高质量图像的潜力，这些投影可以减少患者的X射线剂量。这些MBIR技术依靠投影和反投影来完善图像估计。用于这些基于现代MBIR的技术的一种广泛使用的投影仪称为无分支距离驱动（DD）投影和反投影。尽管此方法可产生高质量的图像，但迭代更新的计算成本使其无法在临床应用中普遍存在。在本文中，我们为使用CUDA编程工具在多GPU系统中同时执行DD投影仪提供了几种新的并行化思想。我们介绍了一些新颖的方案，用于在多个GPU上划分投影数据和图像体素，以避免运行时开销和设备间同步问题。通过消除公共投影平面并将检测器边界直接投影到图像体素边界，我们还降低了算法重叠计算的复杂性。为了减少计算检测器边缘和图像体素边界之间的重叠所需的时间，我们提出了一种预累加技术，以在投影之前和反投影之后的垂直2D图像平板（来自3D图像）中累积图像强度，以确保DD内核在并行GPU线程中运行得更快。为了实施迭代MBIR技术，我们使用交替最小化（AM）算法的并行多GPU版本以及惩罚性似然更新。使用我们建议的带有Siemens Sensation 16位患者扫描数据的重建方法的时间性能显示，使用单个TITAN X GPU的平均速度提高了24倍，使用3个TITAN X GPU的并行速度进行组合投影和反投影的速度提高了74倍。 （C）2017年影像科学与技术学会。

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  • 来源
    《Journal of Imaging Science and Technology》 |2017年第1期|010405.1-010405.13|共13页
  • 作者

    Mitra Ayan; Politte David G.; Whitting Bruce R.; Williamson Jeffrey F.; OSullivan Joseph A.;

  • 作者单位

    Washington Univ, Dept Elect & Syst Engn, 1 Brookings Dr, St Louis, MO 63130 USA;

    Washington Univ, Mallinckrodt Inst Radiol, Sch Med, 510 South Kingshighway Blvd, St Louis, MO 63110 USA;

    Univ Pittsburgh, Dept Radiol, Pittsburgh, PA 15213 USA;

    Virginia Commonwealth Univ, Dept Radiat Oncol, Med Coll Virginia Campus, Richmond, VA 23284 USA;

    Washington Univ, Dept Elect & Syst Engn, 1 Brookings Dr, St Louis, MO 63130 USA;

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