GPU-based iterative transmission reconstruction in 3D ultrasound computer tomography
GPU-based iterative transmission reconstruction in 3D ultrasound computer tomography
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DOI：
10.1016/j.jpdc.2013.09.007
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发表时间：
2014-01-01
影响因子：
3.8
通讯作者：
Becker, J.
中科院分区：
文献类型：
--
作者：
Birk, Matthias;Dapp, Robin;Becker, J.
由于今天的标准筛查方法经常无法在转移发生之前检测到乳腺癌，因此早期诊断仍然是一个重大挑战。随着高质量体积图像的承诺，三维超声计算机断层扫描可能会改善这种情况，但有很高的计算需求。在这项工作中，我们研究了加速的射线为基础的传输重建基于GPU的实现的迭代数值优化算法TVAL 3。我们确定了常规和转置稀疏矩阵向量乘法作为性能限制操作。对于加速重建，我们提出了两个不同的概念，并设计了一个混合方案作为最佳配置。此外，我们还研究了多GPU的可扩展性，并为我们的两个主要用例得出了最佳的设备数量：快速预览模式和高分辨率模式。为了实现一个公平的估计的加速比，我们比较我们的实现优化的CPU版本的算法。使用我们的加速实现，我们重建了一个预览3D体积，其中包含24，576个未知数，体素大小为（8 mm）（3），在0.5 s内约有200，000个方程。在23 s内重建了具有1，572，864个未知数、体素尺寸（2 mm）（3）和约160万个方程的高分辨率体积。与优化的CPU版本相比，这构成了超过一个数量级的加速。(C)2013 Elsevier Inc. All rights reserved.
As today's standard screening methods frequently fail to detect breast cancer before 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. (C) 2013 Elsevier Inc. All rights reserved.