On the usage of the Sparse Fourier Transform in ultrasound propagation simulation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F24%3APU151033" target="_blank" >RIV/00216305:26230/24:PU151033 - isvavai.cz</a>

Výsledek na webu

<a href="https://dl.acm.org/doi/10.1145/3632047.3632064" target="_blank" >https://dl.acm.org/doi/10.1145/3632047.3632064</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3632047.3632064" target="_blank" >10.1145/3632047.3632064</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On the usage of the Sparse Fourier Transform in ultrasound propagation simulation

Popis výsledku v původním jazyce

The Fourier transform is an algorithm for transforming the signal from the space/time domain into the frequency domain. This algorithm is essential for applications like image processing, communication, medicine, differential equations solvers, and many others. In some of these applications, most of the Fourier coefficients are small or equal to zero. This property of the signals is used by the Sparse Fourier transform which estimates significant coefficients of the signal with a lower time complexity than the Fourier transform. The goal of this paper is to evaluate available implementations of the Sparse Fourier transform on a set of benchmarks solving the ultrasound wave propagation in 1D, 2D, and 3D heterogeneous media. The results show that the fastest available implementation in 1D domains is MSFFT, however, it is not possible to use it in our implementation of the 2D Sparse Fourier transform. Thus the AAFFT 0.9 is selected for our implementation of the 2D Sparse Fourier transform as the most stable and acceptably fast implementation. The results on 3D simulation data show, that by using the SpFFT library it is possible to reduce the computation time of the Fourier transform in ultrasound wave propagation simulation.

Název v anglickém jazyce

On the usage of the Sparse Fourier Transform in ultrasound propagation simulation

Popis výsledku anglicky

The Fourier transform is an algorithm for transforming the signal from the space/time domain into the frequency domain. This algorithm is essential for applications like image processing, communication, medicine, differential equations solvers, and many others. In some of these applications, most of the Fourier coefficients are small or equal to zero. This property of the signals is used by the Sparse Fourier transform which estimates significant coefficients of the signal with a lower time complexity than the Fourier transform. The goal of this paper is to evaluate available implementations of the Sparse Fourier transform on a set of benchmarks solving the ultrasound wave propagation in 1D, 2D, and 3D heterogeneous media. The results show that the fastest available implementation in 1D domains is MSFFT, however, it is not possible to use it in our implementation of the 2D Sparse Fourier transform. Thus the AAFFT 0.9 is selected for our implementation of the 2D Sparse Fourier transform as the most stable and acceptably fast implementation. The results on 3D simulation data show, that by using the SpFFT library it is possible to reduce the computation time of the Fourier transform in ultrasound wave propagation simulation.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

ICBRA '23: Proceedings of the 10th International Conference on Bioinformatics Research and Applications

ISBN

979-8-4007-0815-2

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

107-113

Název nakladatele

Association for Computing Machinery

Místo vydání

New York

Místo konání akce

Barcelona

Datum konání akce

22. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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