OpenACC GPU implementation of double-stage delay-multiply-and-sum algorithm

Toward enhanced real-time linear-array photoacoustic tomography

Conference Paper (2019)
Author(s)

Seyyed Reza Miri Rostami (Tarbiat Modares University)

Moein Mozaffarzadeh (Tarbiat Modares University, ImPhys/Acoustical Wavefield Imaging )

Ali Hariri (University of California)

Jesse V. Jokerst (University of California)

Mohsen Ghaffari-Miab (Tarbiat Modares University)

ImPhys/Acoustical Wavefield Imaging
DOI related publication
https://doi.org/10.1117/12.2511115 Final published version
More Info
expand_more
Publication Year
2019
Language
English
ImPhys/Acoustical Wavefield Imaging
Volume number
10878
Article number
108785C
ISBN (electronic)
978-1-5106-2398-9
Event
Photons Plus Ultrasound: Imaging and Sensing 2019 (2019-02-03 - 2019-02-06), San Francisco, United States
Downloads counter
317
Collections
Institutional Repository

Abstract

Double-stage delay-multiply-and-sum (DS-DMAS) is one of the algorithms proposed for photoacoustic image reconstruction where a linear-array transducer is used to detect signals. This algorithm provides a higher contrast image in comparison with the conventional delay-multiply-and-sum (DMAS) and delay-and-sum (DAS), but it imposes a high computational complexity. In this paper, open accelerators (OpenACC) GPU computation parallel approach is used to lessen the computational time and address the high computational time of the DSDMAS for photoacoustic image reconstruction process. Compared with sequential execution of the DS-DMAS on CPU, a speed-up of approximately 74× is achieved (for an image having 1024 × 1024 pixels). The proposed approach provides possibility to have an accurate reconstructed photoacoustic image with a reasonable frame rate. In addition, the higher the number of the image pixels, the higher speed-up is achieved. Using the suggested GPU implementation, it is feasible to reconstruct photoacoustic images having a size of 128 × 128, and 256 × 256 with a frame rate of 3 and 2, respectively.