Accelerating motion-compensated adaptive color Doppler engine on CUDA-based GPU platform

Color Doppler imaging is used to observe the blood flow distribution during doctor´s diagnosis. However, the desired blood signal is greatly affected by clutter noise and probe motions. In previous works, a color Doppler engine was proposed consisting algorithms which can effectively eliminate clutter noises and motion artifacts. Since a large number of data and computations are involved, the color Doppler engine cannot achieve real-time imaging on CPU-based PCs. Therefore, we accelerate the color Doppler engine by parallelizing the executions on many-core GPU. In this work, we explore the parallelism and data locality of the motion-compensated color Doppler algorithms, and implement it on CUDA-based GPU platform. The speedup can be up to 55.5 by using the proposed design methodology.