Recent years have witnessed a rapid advancement in GPU technology, establishing it as a formidable high-performance parallel computing technology with superior floating-point computational capabilities compared to traditional CPUs. This paper explores the application of this technology in the field of photoacoustic imaging, an emerging non-destructive testing technique in biomedical engineering characterized by its high contrast, resolution, and penetration depth. We conduct a data parallelism analysis targeting the computationally intensive image reconstruction segment of photoacoustic imaging. By parallelizing the serial code for iterative reconstruction and optimizing memory access, we achieve significant improvements in processing speed. Our experiments compare the imaging speeds of vascular images reconstructed using CPUs and GPUs, with the results visualized using Matlab. The findings demonstrate that, while maintaining data accuracy, GPU parallel computing methods can markedly accelerate photoacoustic image reconstruction. This acceleration has the potential to facilitate the broader adoption of photoacoustic imaging in applications such as hemodynamic monitoring, clinical disease diagnosis, and drug development.

翻译：近年来，GPU技术迅速发展，已成为一种具有强大高性能并行计算能力的技术，其浮点运算能力远超传统CPU。本文探讨了该技术在光声成像领域的应用，光声成像是一种新兴的生物医学工程无损检测技术，具有高对比度、高分辨率和深穿透深度的特点。我们针对光声成像中计算密集的图像重建部分进行了数据并行性分析。通过将迭代重建的串行代码并行化并优化内存访问，我们显著提升了处理速度。实验比较了使用CPU和GPU重建血管图像的成像速度，结果通过Matlab可视化。研究表明，在保持数据精度的前提下，GPU并行计算方法能够显著加速光声图像重建。这种加速有望促进光声成像在血流动力学监测、临床疾病诊断和药物开发等应用中的更广泛推广。