School of Information Science and Technology, Northwest University, Xi'an, China.
J Xray Sci Technol. 2022;30(4):737-750. doi: 10.3233/XST-221163.
In computed tomography (CT), the total variation (TV) constrained algebraic reconstruction technique (ART) can obtain better reconstruction quality when the projection data are sparse and noisy. However, the ART-TV algorithm remains time-consuming since it requires large numbers of iterations, especially for the reconstruction of high-resolution images. In this work, we propose a fast algorithm to calculate the system matrix for line intersection model and apply this algorithm to perform the forward-projection and back-projection operations of the ART. Then, we utilize the parallel computing techniques of multithreading and graphics processing units (GPU) to accelerate the ART iteration and the TV minimization, respectively. Numerical experiments show that our proposed parallel implementation approach is very efficient and accurate. For the reconstruction of a 2048 × 2048 image from 180 projection views of 2048 detector bins, it takes about 2.2 seconds to perform one iteration of the ART-TV algorithm using our proposed approach on a ten-core platform. Experimental results demonstrate that our new approach achieves a speedup of 23 times over the conventional single-threaded CPU implementation that using the Siddon algorithm.
在计算机断层扫描（CT）中，当投影数据稀疏且存在噪声时，全变差（TV）约束代数重建技术（ART）可以获得更好的重建质量。然而，ART-TV 算法仍然很耗时，因为它需要大量的迭代次数，特别是对于高分辨率图像的重建。在这项工作中，我们提出了一种快速算法来计算线交点模型的系统矩阵，并将该算法应用于 ART 的前向投影和后向投影操作。然后，我们利用多线程和图形处理单元（GPU）的并行计算技术分别加速 ART 迭代和 TV 最小化。数值实验表明，我们提出的并行实现方法非常高效和准确。对于从 2048 个探测器箱的 180 个投影视图重建 2048×2048 图像，在具有十个内核的平台上，使用我们提出的方法对 ART-TV 算法执行一次迭代大约需要 2.2 秒。实验结果表明，与使用 Siddon 算法的传统单线程 CPU 实现相比，我们的新方法实现了 23 倍的加速。
