ffdas: Volumetric ultrasound reconstruction at warp speed

Volumetric ultrafast ultrasound imaging demands reconstruction of images with millions of voxels thousands of times per second, creating computational challenges that limit both real-time feedback and easy offline analysis. Graphics processing units (GPUs) are well suited to this workload, yet we show that standard delay-and-sum implementations underutilize GPU resources through fragmented memory access patterns, even when sufficient computational capacity is available. Three optimization strategies address this: aligning memory access with GPU transfer granularity, halving memory traffic through mixed-precision storage, and exploiting spatial locality to utilize tensor core arithmetic. Together, these achieve kilohertz frame rates for $128^3$-voxel grids with 1024-element arrays, substantially outperforming existing implementations while maintaining image quality. This enables real-time volumetric imaging at scales previously restricted to offline processing, supporting applications such as intraoperative brain imaging and brain-computer interfaces where immediate feedback is essential. We release our implementation as part of the open-source \texttt{ffdas} library.

Source: arXiv:2606.13259v1 - http://arxiv.org/abs/2606.13259v1 PDF: https://arxiv.org/pdf/2606.13259v1 Original Link: http://arxiv.org/abs/2606.13259v1