Fast GPU-beamforming of Row-Column Addressed Probe Data

A delay-and-sum beamformer for 3D imaging using row-column arrays and written in CUDA is presented and compared to an existing similar GPU-based beamformer written in the MATLAB programming language. Data from a 192+192 row-column array single element emission sequence is simulated and beamformed. T...

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Bibliographic Details
Published in:IEEE International Ultrasonics Symposium (Online) pp. 1497 - 1500
Main Authors: Stuart, Matthias Bo, Jensen, Patrick Moller, Olsen, Julian Thomas Reckeweg, Kristensen, Alexander Borch, Schou, Mikkel, Dammann, Bernd, Sorensen, Hans Henrik Brandenborg, Jensen, Jorgen Arendt
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2019
Subjects:
Array signal processing
Arrays
Delays
Graphics processing units
Imaging
Matlab
Throughput
ISSN:1948-5727
Online Access:Get full text
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Summary:A delay-and-sum beamformer for 3D imaging using row-column arrays and written in CUDA is presented and compared to an existing similar GPU-based beamformer written in the MATLAB programming language. Data from a 192+192 row-column array single element emission sequence is simulated and beamformed. The two beamformers' performance is evaluated in two synthetic aperture setups comprised of 1) two orthogonal planes and 2) a full volume on three different NVIDIA GPUs: a 1050 Ti, a 1080 Ti, and a TITAN V. The execution time and the sample throughput (samples beamformed per second) are reported. The CUDA beamformer performs consistently better than the MATLAB beamformer with speed-ups ranging from 1.9 to 64.6 times, and the worst-case throughput of the CUDA beamformer exceeds the best-case of the MATLAB beamformer. High-resolution images of crossing planes can be beamformed at up to 13 Hz, while a 50-by-50-by-20 cubic-millimeter high-resolution volume sampled at one quarter of a millimeter is beamformed in 3 seconds.
ISSN:1948-5727
DOI:10.1109/ULTSYM.2019.8925802