High volume acquisition rates are imperative for certain medical ultrasound imaging applications, such as 3D elastography and 3D vector flow imaging. As ultrasound imaging transitions from 2D to 3D, the massive data bandwidth and billions of trigonometric operations required to reconstruct each volume leaves conventional computer architectures falling short. Despite recent algorithmic improvements, high-volume-rate ultrasound imaging remains computationally infeasible on known platforms. In this article, we expand our previous work on Tetris, a novel hardware accelerator for separable ultrasound beamforming that enables volume acquisition rates up to the physics limits of acoustic propagation delay. Through algorithmic and hardware optimizations, we enable an image reconstruction system design outclassing previously proposed accelerators in performance while lowering hardware complexity, storage, and power requirements. Tetris operates in a streaming fashion—without requiring on-chip storage of the entire receive signal—reconstructing volumes in real-time. For a representative imaging task, our proposed system generates physics-limited 13,000 volumes per second in a 2 watt power budget. The Tetris beamformer has an unprecedented power efficiency of 2.03 tera-beamforming operations per watt—an increase in efficiency of nearly 3× compared to the prior work.

中文翻译：

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TETRIS：使用软件/硬件协同设计实现手持式、物理受限的 3D 平面波超声成像

对于某些医学超声成像应用，例如 3D 弹性成像和 3D 矢量流成像，高容量采集率是必不可少的。随着超声成像从 2D 过渡到 3D，重建每个体积所需的海量数据带宽和数十亿次三角运算使传统的计算机架构无法满足要求。尽管最近的算法有所改进，但在已知平台上，高容积率超声成像在计算上仍然不可行。在本文中，我们扩展了我们之前在俄罗斯方块上的工作，俄罗斯方块是一种用于可分离超声波波束成形的新型硬件加速器，可使体积采集速率达到声学传播延迟的物理极限。通过算法和硬件优化，我们使图像重建系统设计在性能上优于先前提出的加速器，同时降低了硬件复杂性、存储和功率要求。俄罗斯方块以流媒体方式运行——不需要整个接收信号的片上存储——实时重建卷。对于具有代表性的成像任务，我们提出的系统在 2 瓦的功率预算中每秒生成 13,000 卷物理限制。俄罗斯方块波束成形器具有前所未有的功率效率，每瓦可进行 2.03 兆兆波束成形操作——与之前的工作相比，效率提高了近 3 倍。对于具有代表性的成像任务，我们提出的系统以 2 瓦的功率预算每秒生成 13,000 卷物理限制。俄罗斯方块波束成形器具有前所未有的功率效率，每瓦可进行 2.03 兆兆波束成形操作——与之前的工作相比，效率提高了近 3 倍。对于具有代表性的成像任务，我们提出的系统以 2 瓦的功率预算每秒生成 13,000 卷物理限制。俄罗斯方块波束成形器具有前所未有的功率效率，每瓦可进行 2.03 兆兆波束成形操作——与之前的工作相比，效率提高了近 3 倍。