用消费级显卡实现每秒1.1万亿点的超快超声成像，首次实现实时3D超声重建。
mach: ultrafast ultrasound beamforming
- 采用混合延迟计算与共享内存优化，大幅降低内存开销。
- 在消费级GPU上达1.1万亿点/秒，比现有开源工具快10倍以上。
- 适合需要实时3D超声的应用，如神经功能成像和术中导航。
三维超快超声生成海量数据，具有高帧率、密集重构网格和大通道数特征。传统束形成算法计算负担重，限制研究效率并阻碍弹性成像、功能神经成像和显微成像等新兴模态的实时应用。我们开发了mach，一个开源、GPU加速的束形成器，其包含高度优化的延迟-求和CUDA内核和易用的Python接口。mach采用混合延迟计算策略，相比全预计算方法显著降低内存开销；CUDA实现通过优化内存布局实现合并访问，并利用共享内存复用多帧间的延迟计算。我们在PyMUST旋转盘数据集上进行基准测试，并与现有开源束形成器对比验证数值精度。结果表明，mach在消费级GPU上达到每秒1.1万亿点处理速度，性能优于现有开源GPU束形成器10倍以上。在PyMUST旋转盘基准测试中，重建耗时仅0.23毫秒，比声波往返成像深度的时间快6倍。与其它束形成器对比验证显示，功率多普勒误差低于-60 dB，B模式误差低于-120 dB。结论：mach实现了每秒1.1万亿点的吞吐量，首次在消费级硬件上实现3D超快超声的实时重建。通过消除束形成瓶颈，mach使3D功能神经成像、术中引导和超声定位显微镜等实时应用成为可能。代码已开源：https://github.com/Forest-Neurotech/mach
原文摘要 · Abstract (English)
Purpose: Volumetric ultrafast ultrasound produces massive datasets with high frame rates, dense reconstruction grids, and large channel counts. Beamforming computational demands limit research throughput and prevent real-time applications in emerging modalities such as elastography, functional neuroimaging, and microscopy. Approach: We developed mach, an open-source, GPU-accelerated beamformer with a highly optimized delay-and-sum CUDA kernel and an accessible Python interface. mach uses a hybrid delay computation strategy that substantially reduces memory overhead compared to fully precomputed approaches. The CUDA implementation optimizes memory layout for coalesced access and reuses delay computations across frames via shared memory. We benchmarked mach on the PyMUST rotating disk dataset and validated numerical accuracy against existing open-source beamformers. Results: mach processes 1.1 trillion points per second on a consumer-grade GPU, achieving $>$10$\times$ faster performance than existing open-source GPU beamformers. On the PyMUST rotating disk benchmark, mach completes reconstruction in 0.23~ms, 6$\times$ faster than the acoustic round-trip time to the imaging depth. Validation against other beamformers confirms numerical accuracy with errors below $-60$~dB for Power Doppler and $-120$~dB for B-mode. Conclusions: mach achieves 1.1 trillion points per second throughput, enabling real-time 3D ultrafast ultrasound reconstruction for the first time on consumer-grade hardware. By eliminating the beamforming bottleneck, mach enables real-time applications such as 3D functional neuroimaging, intraoperative guidance, and ultrasound localization microscopy. mach is freely available at https://github.com/Forest-Neurotech/mach
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