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GPU-based iterative transmission reconstruction in 3D ultrasound computer tomography

机译:3D超声计算机层析成像中基于GPU的迭代传输重建

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摘要

As today s standard screening metnods frequently tan to detect Breast cancer Derore metastases have developed, early diagnosis is still a major challenge. With the promise of high-quality volume images, three-dimensional ultrasound computer tomography is likely to improve this situation, but has high computational needs. In this work, we investigate the acceleration of the ray-based transmission reconstruction by a GPU-based implementation of the iterative numerical optimization algorithm TVAL3. We identified the regular and transposed sparse-matrix-vector multiply as the performance limiting operations. For accelerated reconstruction we propose two different concepts and devise a hybrid scheme as optimal configuration. In addition we investigate multi-GPU scalability and derive the optimal number of devices for our two primary use-cases: a fast preview mode and a high-resolution mode. In order to achieve a fair estimation of the speedup, we compare our implementation to an optimized CPU version of the algorithm. Using our accelerated implementation we reconstructed a preview 3D volume with 24,576 unknowns, a voxel size of (8 mm)~3 and approximately 200,000 equations in 0.5 s. A high-resolution volume with 1,572,864 unknowns, a voxel size of (2mm)~3 and approximately 1.6 million equations was reconstructed in 23 s. This constitutes an acceleration of over one order of magnitude in comparison to the optimized CPU version.
机译:随着当今标准的筛查方法经常被晒黑以检测乳腺癌Derore转移灶的发展,早期诊断仍然是一个重大挑战。有了高质量体图像的承诺,三维超声计算机断层扫描可能会改善这种情况,但是具有很高的计算需求。在这项工作中,我们通过迭代数值优化算法TVAL3的基于GPU的实现来研究基于射线的传输重建的加速。我们将规则和转置的稀疏矩阵矢量乘法确定为性能限制操作。为了加快重建速度,我们提出了两个不同的概念,并设计了一种混合方案作为最佳配置。此外,我们研究了多GPU的可扩展性,并针对我们的两个主要用例(快速预览模式和高分辨率模式)得出了最佳的设备数量。为了公平地估计加速,我们将实现与算法的优化CPU版本进行了比较。使用我们的加速实现,我们重建了一个预览的3D体积,其中包含24,576个未知数,体素大小(8 mm)〜3和0.5 s内约200,000个方程。在23 s内重建了高分辨率的体积,其中包含1,572,864个未知数,体素大小(2mm)〜3和大约160万个方程。与优化的CPU版本相比,这构成了一个数量级以上的加速。

著录项

  • 来源
    《Journal of Parallel and Distributed Computing》 |2014年第1期|1730-1743|共14页
  • 作者单位

    Institute for Data Processing and Electronics (IPE), Karlsruhe Institute of Technology, Herrmann-von-Helmholtsplatz 1, 76334 Eggenstein-Leopoldshafen, Germany;

    Institute for Data Processing and Electronics (IPE), Karlsruhe Institute of Technology, Herrmann-von-Helmholtsplatz 1, 76334 Eggenstein-Leopoldshafen, Germany;

    Institute for Data Processing and Electronics (IPE), Karlsruhe Institute of Technology, Herrmann-von-Helmholtsplatz 1, 76334 Eggenstein-Leopoldshafen, Germany;

    Institute for Information Processing Technologies (ITIV), Karlsruhe Institute of Technology, Engesserstr. 5,76131 Karlsruhe, Germany;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    GPU; Ultrasound imaging; Application acceleration; SpMV; Numerical optimization;

    机译:GPU;超声成像;应用程序加速;SpMV;数值优化;

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