Real-Time Volumetric Synthetic Aperture Software Beamforming of Row-Column Probe Data

Two delay-and-sum beamformers for 3-D synthetic aperture imaging with row-column addressed arrays are presented. Both beamformers are software implementations for graphics processing unit (GPU) execution with dynamic apodizations and third-order polynomial subsample interpolation. The first beamform...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on ultrasonics, ferroelectrics, and frequency control. - 1986. - 68(2021), 8 vom: 08. Aug., Seite 2608-2618
1. Verfasser: Stuart, Matthias Bo (VerfasserIn)
Weitere Verfasser: Jensen, Patrick Moller, Olsen, Julian Thomas Reckeweg, Kristensen, Alexander Borch, Schou, Mikkel, Dammann, Bernd, Sorensen, Hans Henrik Brandenborg, Jensen, Jorgen Arendt
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2021
Zugriff auf das übergeordnete Werk:IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Schlagworte:Journal Article Research Support, Non-U.S. Gov't
Beschreibung
Zusammenfassung:Two delay-and-sum beamformers for 3-D synthetic aperture imaging with row-column addressed arrays are presented. Both beamformers are software implementations for graphics processing unit (GPU) execution with dynamic apodizations and third-order polynomial subsample interpolation. The first beamformer was written in the MATLAB programming language and the second was written in C/C++ with the compute unified device architecture (CUDA) extensions by NVIDIA. Performance was measured as volume rate and sample throughput on three different GPUs: a 1050 Ti, a 1080 Ti, and a TITAN V. The beamformers were evaluated across 112 combinations of output geometry, depth range, transducer array size, number of virtual sources, floating-point precision, and Nyquist rate or in-phase/quadrature beamforming using analytic signals. Real-time imaging defined as more than 30 volumes per second was attained by the CUDA beamformer on the three GPUs for 13, 27, and 43 setups, respectively. The MATLAB beamformer did not attain real-time imaging for any setup. The median, single-precision sample throughput of the CUDA beamformer was 4.9, 20.8, and 33.5 Gsamples/s on the three GPUs, respectively. The throughput of CUDA beamformer was an order of magnitude higher than that of the MATLAB beamformer
Beschreibung:Date Completed 30.09.2021
Date Revised 30.09.2021
published: Print-Electronic
Citation Status PubMed-not-MEDLINE
ISSN:1525-8955
DOI:10.1109/TUFFC.2021.3071810