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首页> 外文期刊>IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control >A Real-Time, GPU-Based Implementation of Aperture Domain Model Image REconstruction
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A Real-Time, GPU-Based Implementation of Aperture Domain Model Image REconstruction

机译：基于GPU的孔径域模型图像重建的实时实现

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Multipath and off-axis scattering are two of the primary mechanisms for ultrasound image degradation. To address their impact, we have proposed Aperture Domain Model Image REconstruction (ADMIRE). This algorithm utilizes a model-based approach in order to identify and suppress sources of acoustic clutter. The ability of ADMIRE to suppress clutter and improve image quality has been demonstrated in previous works, but its use for real-time imaging has been infeasible due to its significant computational requirements. However, in recent years, the use of graphics processing units (GPUs) for general-purpose computing has enabled the significant acceleration of compute-intensive algorithms. This is because many modern GPUs have thousands of computational cores that can be utilized to perform massively parallel processing. Therefore, in this work, we have developed a GPU-based implementation of ADMIRE. The implementation on a single GPU provides a speedup of two orders of magnitude when compared to a serial CPU implementation, and additional speedup is achieved when the computations are distributed across two GPUs. In addition, we demonstrate the feasibility of the GPU implementation to be used for real-time imaging by interfacing it with a Verasonics Vantage 128 ultrasound research system. Moreover, we show that other beamforming techniques, such as delay-and-sum (DAS) and short-lag spatial coherence (SLSC), can be computed and simultaneously displayed with ADMIRE. The frame rate depends upon various parameters, and this is exhibited in the multiple imaging cases that are presented. An open-source code repository containing CPU and GPU implementations of ADMIRE is also provided.
机译：多路径和轴外散射是超声图像劣化的两个主要机制。为了解决他们的影响，我们已经提出了Aperture域模型图像重建（欣赏）。该算法利用基于模型的方法来识别和抑制声学杂波的源。在以前的作品中已经证明了令人钦佩抑制杂波和改善图像质量的能力，但由于其显着的计算要求，其对实时成像的使用是不可行的。但是，近年来，使用图形处理单元（GPU）对通用计算使得计算密集型算法的显着加速。这是因为许多现代GPU具有成千上万的计算核心，可以用于执行大规模并行处理。因此，在这项工作中，我们制定了基于GPU的欣赏的实施。与串行CPU实现相比，单个GPU上的实现提供了两个数量级的加速，并且当计算在两个GPU上分布计算时，实现了额外的加速。此外，我们展示了GPU实现的可行性，通过将其与Arasonics Vantage 128超声研究系统相互作用来用于实时成像。此外，我们示出了可以使用令人欣赏的和同时显示诸如延迟和和（DAS）和短滞后空间相干性（SLSC）的其他波束成形技术。帧速率取决于各种参数，并且这在呈现的多个成像情况下展示。还提供了包含欣赏的CPU和GPU实现的开放源代码存储库。

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