Design Space Exploration of Adaptive Beamforming Acceleration for Bedside and Portable Medical Ultrasound Imaging

摘要

The use of adaptive beamforming is a viable solution to provide high-resolution real-time medical ultrasound imaging. However, the increase in image resolution comes at an expense of a significant increase in compute requirement over conventional algorithms. In a bedside diagnosis setting where plug-in power is available, GPUs are promising accelerators to address the processing demand. However, in the case of point-of-care diagnostics where portable ultrasound imaging devices must be used, alternative power-efficient computer systems must be employed, possibly at the expense of lower image resolution in order to maintain real-time performance. This paper presents an initial design space exploration on viable compute architectures that might address the drastically different requirements between bedside and portable medical ultrasound imaging systems using adaptive beamforming. The design and implementation of a GPU accelerator that provides over 45x performance improvement over the equivalent C implementation on a single CPU is presented. Furthermore, and implementation of the beamforming algorithm on a high-performance mobile platform based on an ARM Cortex A8 mobile processor in combination with the built-in NEON accelerator is also presented. The mobile platform delivers over 270x reduction in power consumption when compared to the GPU platform at an expense of much reduced performance. The tradeoffs between power, performance and image quality among the target platforms are studied and future research directions in power-efficient architectures for high-performance medical ultrasound systems are presented.