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Ultrasound parallel delay multiply and sum beamforming algorithm based on GPU

机译:基于GPU的超声并行延迟乘加和波束形成算法

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In 2015, Giulia Matrone proposed a nonlinear beamforming algorithm, Delay Multiply and Sum Beamforming (DMAS), which is superior to the Capon algorithm and Delay and Sum Beamforming (DAS) algorithm in resolution and contrast. The complexity of the DMAS algorithm is O(n2). In this paper, a parallel beamforming algorithm named PDMAS is proposed. The complexity of the PDMAS algorithm is O(n). In PMDAS algorithm, the channel operation is independent with each other and with good parallelism. At the same time, the software architecture of PDMAS algorithm is designed on GPU by using OpenCL programming language to test the performance of the proposed algorithm. The experiments are performed on the Precision T7810 workstation which contains two CPUs (Intel (R) Xeon (R) CPU E5-2620 v4) and AMD FirePro W7100 GPU respectively. The experimental result shows that both the PDMAS and the DMAS algorithm can't meet the real-time imaging requirements on the CPU. On the GPU, the frame rate of the DMAS algorithm is greatly improved, but still can't meet the real-time imaging requirements. On the other hand, the frame rate of PDMAS algorithm on the GPU can reach to 83fps, which means can fully meet the needs of real-time imaging. It can be concluded that the PDMAS algorithm has a better performance on GPU and fully meet the requirements of ultrasound real-time imaging systems.
机译:2015年,Giulia Matrone提出了一种非线性波束成形算法,即延迟相乘和求和波束成形(DMAS),它在分辨率和对比度方面优于Capon算法和延迟与求和波束成形(DAS)算法。 DMAS算法的复杂度为O(n 2 )。本文提出了一种并行波束成形算法,称为PDMAS。 PDMAS算法的复杂度为O(n)。在PMDAS算法中,通道操作彼此独立并且具有良好的并行性。同时,利用OpenCL编程语言在GPU上设计了PDMAS算法的软件架构,以测试该算法的性能。实验在Precision T7810工作站上执行,该工作站分别包含两个CPU(英特尔(R)至强(R)CPU E5-2620 v4)和AMD FirePro W7100 GPU。实验结果表明,PDMAS和DMAS算法均不能满足CPU的实时成像要求。在GPU上,虽然DMAS算法的帧速率有了很大的提高,但仍然不能满足实时成像的要求。另一方面,GPU上的PDMAS算法的帧速率可以达到83fps,这可以完全满足实时成像的需求。可以得出结论,PDMAS算法在GPU上具有更好的性能,完全可以满足超声实时成像系统的要求。

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