Texas A&M University, Dwight Look College of Engineering, Department of Electrical and Computer Engineering, College Station, TX, USA.
IEEE Trans Ultrason Ferroelectr Freq Control. 2011 Dec;58(12):2631-45. doi: 10.1109/TUFFC.2011.2126.
Ultrasound elastography is becoming a widely available clinical imaging tool. In recent years, several real- time elastography algorithms have been proposed; however, most of these algorithms achieve real-time frame rates through compromises in elastographic image quality. Cross-correlation- based elastographic techniques are known to provide high- quality elastographic estimates, but they are computationally intense and usually not suitable for real-time clinical applications. Recently, the use of massively parallel general purpose graphics processing units (GPGPUs) for accelerating computationally intense operations in biomedical applications has received great interest. In this study, we investigate the use of the GPGPU to speed up generation of cross-correlation-based elastograms and achieve real-time frame rates while preserving elastographic image quality. We propose and statistically analyze performance of a new hybrid model of computation suitable for elastography applications in which sequential code is executed on the CPU and parallel code is executed on the GPGPU. Our results indicate that the proposed hybrid approach yields optimal results and adequately addresses the trade-off between speed and quality.
超声弹性成像是一种广泛应用的临床成像工具。近年来，已经提出了几种实时弹性成像算法；然而，这些算法中的大多数通过在弹性图像质量上做出妥协来实现实时帧率。基于互相关的弹性技术已知可提供高质量的弹性估计，但它们计算量很大，通常不适合实时临床应用。最近，使用大规模并行通用图形处理单元（GPGPU）来加速生物医学应用中的计算密集型操作引起了极大的兴趣。在这项研究中，我们研究了使用 GPGPU 来加速基于互相关的弹性图的生成，并实现实时帧率，同时保持弹性图像质量。我们提出并统计分析了一种新的混合计算模型的性能，该模型适用于弹性成像应用，其中顺序代码在 CPU 上执行，并行代码在 GPGPU 上执行。我们的结果表明，所提出的混合方法可获得最佳结果，并充分解决了速度和质量之间的权衡问题。
