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首页> 外文期刊>IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control >Fast Simulation of Dynamic Ultrasound Images Using the GPU
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Fast Simulation of Dynamic Ultrasound Images Using the GPU

机译：使用GPU快速仿真动态超声图像

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Simulated ultrasound data is a valuable tool for development and validation of quantitative image analysis methods in echocardiography. Unfortunately, simulation time can become prohibitive for phantoms consisting of a large number of point scatterers. The COLE algorithm by Gao et al. is a fast convolution-based simulator that trades simulation accuracy for improved speed. We present highly efficient parallelized CPU and GPU implementations of the COLE algorithm with an emphasis on dynamic simulations involving moving point scatterers. We argue that it is crucial to minimize the amount of data transfers from the CPU to achieve good performance on the GPU. We achieve this by storing the complete trajectories of the dynamic point scatterers as spline curves in the GPU memory. This leads to good efficiency when simulating sequences consisting of a large number of frames, such as B-mode and tissue Doppler data for a full cardiac cycle. In addition, we propose a phase-based subsample delay technique that efficiently eliminates flickering artifacts seen in B-mode sequences when COLE is used without enough temporal oversampling. To assess the performance, we used a laptop computer and a desktop computer, each equipped with a multicore Intel CPU and an NVIDIA GPU. Running the simulator on a high-end TITAN X GPU, we observed two orders of magnitude speedup compared to the parallel CPU version, three orders of magnitude speedup compared to simulation times reported by Gao et al. in their paper on COLE, and a speedup of 27000 times compared to the multithreaded version of Field II, using numbers reported in a paper by Jensen. We hope that by releasing the simulator as an open-source project we will encourage its use and further development.
机译：模拟超声数据是开发和验证超声心动图定量图像分析方法的宝贵工具。不幸的是，对于由大量点散射体组成的体模，仿真时间可能变得令人望而却步。 Gao等人的COLE算法。是一种基于卷积的快速仿真器，它以提高仿真速度为代价。我们介绍了COLE算法的高效并行CPU和GPU实现，重点是涉及移动点散射体的动态仿真。我们认为，至关重要的是，最大程度地减少从CPU传输的数据量，以在GPU上获得良好的性能。我们通过将动态点散射体的完整轨迹作为样条曲线存储在GPU内存中来实现。当模拟由大量帧组成的序列（例如，整个心动周期的B模式和组织多普勒数据）时，这会带来良好的效率。此外，我们提出了一种基于相位的子采样延迟技术，该技术可有效消除使用COLE时在B模式序列中看到的闪烁伪像，而无需进行足够的时间过采样。为了评估性能，我们使用了膝上型计算机和台式计算机，每个计算机都配备了多核Intel CPU和NVIDIA GPU。在高端TITAN X GPU上运行模拟器，与并行CPU版本相比，我们观察到两个数量级的加速，与Gao等人报道的模拟时间相比，三个数量级的加速。在他们关于COLE的论文中，使用Jensen在论文中报告的数字，与Field II的多线程版本相比，加速了27000倍。我们希望通过将模拟器作为一个开源项目发布，来鼓励它的使用和进一步开发。

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