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首页> 外文期刊>IEEE Transactions on Medical Imaging >Multi-Operator Minimum Variance Adaptive Beamforming Algorithms Accelerated With GPU
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Multi-Operator Minimum Variance Adaptive Beamforming Algorithms Accelerated With GPU

机译：多操作员最小方差自适应波束成形算法加速了GPU

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摘要

The goal of this work is to design high-resolution, high-contrast and robust MV adaptive beamforming algorithms, which are also implemented in real-time frame rate. Multi-operator optimization is introduced into MV adaptive beamforming in this work to propose a multi-operator MV adaptive beamforming algorithmic optimization framework. Based on the proposed algorithmic optimization framework, the algorithm optimization can be either conducted by activating a single optimization operator, or conducted by activating multiple optimization operators. The multi-operator MV (MOMV) adaptive beamforming algorithms are then derived from this framework. Moreover, in order to promote the real-time imaging capability of MOMV beamforming, a GPU-based parallel acceleration framework is proposed along with the algorithmic optimization framework by exploring the image-level coarse-grained parallelization and pixel-level fine-grained parallelization. GPU computing resource allocation strategy and memory access strategy are both explored to better design the acceleration framework. Comprehensive quantitative simulation evaluations and qualitative in vivo experiments of imaging performance are studied, and the results demonstrate that the proposed MOMV adaptive beamforming algorithms significantly improve the imaging performance as compared with other MV beamforming algorithms, which have high resolution, high contrast, good robustness, and real-time imaging capability with thousands of acceleration speedup. Furthermore, the MOMV beamforming algorithm without eigen-decomposition and projection optimization operator achieves much higher beamforming frame rate with little downgrade of image quality as compared with the MOMV beamforming algorithm with all optimization operators.
机译：这项工作的目标是设计高分辨率，高对比度和强大的MV自适应波束形成算法，该算法也以实时帧速率实现。在这项工作中引入了多操作员优化进入MV自适应波束成形，提出了一种多操作员MV自适应波束形成算法优化框架。基于所提出的算法优化框架，可以通过激活单个优化操作员来进行算法优化，或者通过激活多个优化运算符来进行。然后从该框架中导出多操作员MV（MOMV）自适应波束形成算法。此外，为了促进MOMV波束成形的实时成像能力，通过探索图像级粗粒度并行化和像素级细粒度并行化，提出基于GPU的并行加速框架以及算法优化框架。 GPU计算资源分配策略和内存访问策略都探讨了更好地设计加速框架。研究了综合定量模拟评估和体内成像性能实验的定性，结果表明，与其他MV波束形成算法相比，所提出的MOMV自适应波束形成算法显着提高了成像性能，具有高分辨率，高对比度，较高的鲁棒性，和数千个加速度的实时成像功能。此外，与具有所有优化操作员的MOMV波束形成算法相比，没有特征分解和投影优化操作员的MOMV波束成形算法的比较高度的波束形成帧速率，与MOMV波束形成算法相比很少降级。

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