Parallel Techniques for Estimation and Correction of Aberration in Medical Ultrasound Imaging

Abstract

Medical ultrasound imaging is a great diagnostic tool for physicians because of its noninvasive nature. It is performed by directing ultrasonic sound into tissue and visualizing the echo signal. Aberration in the reflected signal is caused by inhomogeneous tissue varying the speed of sound, which results in a blurring of the image. Dr. Måsøy and Dr. Varslot at NTNU have developed and algorithm for estimating and correcting ultrasound aberration. This algorithm adaptively estimates the aberration and adjusts the next transmitted signal to account for the aberration, resulting in a clearer image. This master's thesis focuses on developing a parallelized version of this algorithm. Since NVIDIA CUDA (Compute Unified Device Architecture) is an architecture oriented towards general purpose computations on the GPU (Graphics Processing Unit), it also examines how suitable the parallelization is for modern GPUs. The goal is using the GPU to off-load the CPU with an aim of achieving real-time calculations of the correction filter. The ultrasound image creation is examined, including how the aberrations come into being. Next, how the algorithm can be implemented efficiently using the GPU is looked at using both NVIDIA's FFT (fast Fourier transform) library as well as developing several computational kernels to run on the GPU. Our findings show that the algorithm is highly parallelizable and achieves a speedup of over 5x when implemented on the GPU. This is, however, not fast enough for real-time correction, but taking into account suggestions for overcoming the limitations encountered, the study shows great promise for future work.