GPU Acceleration of Wave Based Transmission Tomography

Abstract

To accelerate the process of 3D ultrasound computed tomography, we parallelize the most time-consuming part of a paraxial forward model on GPU, where massive complex multiplications and 2D Fourier transforms have to be performed iteratively. We test our GPU implementation on a synthesized symmetric breast phantom with different sizes. In the best case, for only one emitter position, the speedup of a desktop GPU reaches 23 times when the data transfer time is included, and 100 times when only GPU parallel computing time is considered. In the worst case, the speedup of a less powerful laptop GPU is still 2.5 times over a six-core desktop CPU, when the data transfer time is included. For the correctness of the values computed on GPU, the maximum percent deviation of L2 norm is only 0.014%.