Appl Opt. 2020 Jan 20;59(3):712-719. doi: 10.1364/AO.378466.
Photoacoustic computed tomography with compressed sensing (CS-PACT) is a commonly used imaging strategy for sparse-sampling PACT. However, it is very time-consuming because of the iterative process involved in the image reconstruction. In this paper, we present a graphics processing unit (GPU)-based parallel computation framework for total-variation-based CS-PACT and adapted into a custom-made PACT system. Specifically, five compute-intensive operators are extracted from the iteration algorithm and are redesigned for parallel performance on a GPU. We achieved an image reconstruction speed 24-31 times faster than the CPU performance. We performed in vivo experiments on human hands to verify the feasibility of our developed method.
基于压缩感知的光声计算机断层成像（CS-PACT）是稀疏采样 PACT 常用的成像策略。但是，由于图像重建涉及迭代过程，因此非常耗时。在本文中，我们提出了一种基于图形处理单元（GPU）的总变分 CS-PACT 并行计算框架，并将其应用于定制的 PACT 系统。具体来说，从迭代算法中提取了五个计算密集型算子，并对其进行了重新设计，以便在 GPU 上实现并行性能。我们实现了比 CPU 性能快 24-31 倍的图像重建速度。我们在人手上进行了体内实验，以验证我们开发的方法的可行性。
