基于图形处理器的三维光声层析成像图像加速重建
Accelerating Image Reconstruction in Three-Dimensional Optoacoustic Tomography on Graphics Processing Units
摘要：
目的：光声层析成像（OAT）本质上是一个三维（3D）逆问题。然而，大多数OAT图像重建的研究仍然采用二维（2D）成像模型。一个重要原因是因为3D图像重建在计算上是繁重的。这项工作的目的是加速现有的图像重建算法的三维OAT通过使用并行编程技术。研究方法：提出了加速滤波反投影（FBP）算法和对应于两种不同数值成像模型的两对不同的投影/反投影操作的并行化策略。这些算法旨在充分利用图形处理单元（GPU）的并行计算能力。为了评估投影/反投影对的并行化策略，实现了迭代图像重建算法。计算机模拟和实验研究进行调查的计算效率和数值精度的算法。结果如下：GPU的实现提高了计算效率的因子1000，125，和250的FBP算法和两对投影/反投影算子，分别。通过使用FBP和迭代图像重建算法，从计算机模拟和实验数据中重建准确的图像。结论：首次提出了三维OAT图像重建的并行化策略。这些基于GPU的实现显著减少了3D图像重建的计算时间，补充了我们早期在3D OAT迭代图像重建方面的工作。
英文摘要：
Purpose: Optoacoustic tomography (OAT) is inherently a three-dimensional (3D) inverse problem. However, most studies of OAT image reconstruction still employ two-dimensional (2D) imaging models. One important reason is because 3D image reconstruction is computationally burdensome. The aim of this work is to accelerate existing image reconstruction algorithms for 3D OAT by use of parallel programming techniques. Methods: Parallelization strategies are proposed to accelerate a filtered backprojection (FBP) algorithm and two different pairs of projection/backprojection operations that correspond to two different numerical imaging models. The algorithms are designed to fully exploit the parallel computing power of graphic processing units (GPUs). In order to evaluate the parallelization strategies for the projection/backprojection pairs, an iterative image reconstruction algorithm is implemented. Computer-simulation and experimental studies are conducted to investigate the computational efficiency and numerical accuracy of the developed algorithms. Results: The GPU implementations improve the computational efficiency by factors of 1, 000, 125, and 250 for the FBP algorithm and the two pairs of projection/backprojection operators, respectively. Accurate images are reconstructed by use of the FBP and iterative image reconstruction algorithms from both computer-simulated and experimental data. Conclusions: Parallelization strategies for 3D OAT image reconstruction are proposed for the first time. These GPU-based implementations significantly reduce the computational time for 3D image reconstruction, complementing our earlier work on 3D OAT iterative image reconstruction.
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