Publication detail

Techniques for Efficient Fourier Transform Computation in Ultrasound Simulations

OLŠÁK, O. JAROŠ, J.

Original Title

Techniques for Efficient Fourier Transform Computation in Ultrasound Simulations

Type

article in a collection out of WoS and Scopus

Language

English

Original Abstract

Noninvasive ultrasound surgeries represent a rapidly growing field in medical applications. Preoperative planning often relies on computationally expensive ultrasound simulations. This paper explores methods to accelerate these simulations by reducing the computation time of the Fourier transform, which is an integral part of the simulation in the k-Wave toolbox. Two experiments and their results will be presented. The first investigates substituting the standard Fast Fourier Transform (FFT) with a Sparse Fourier Transform (SFT). The second approach utilises filtering of the frequency spectrum, inspired by image compression algorithms. The aim of both experiments is to find a suitable method for accelerating the Fourier transform while utilising the sparsity of the spectrum in acoustic pressure. Our findings show that filtering offers significantly better results in terms of computation error, leading to a substantial reduction in overall simulation runtime.

Keywords

Ultrasound wave propagation, k-Wave, Sparse Fourier Transform

Authors

OLŠÁK, O.; JAROŠ, J.

Released

30. 8. 2024

Publisher

Association for Computing Machinery

Location

New York

ISBN

979-8-4007-0413-0

Book

HPDC '24: Proceedings of the 33nd International Symposium on High-Performance Parallel and Distributed Computing

Pages from

361

Pages to

363

Pages count

3

URL

https://dl.acm.org/doi/10.1145/3625549.3658825

BibTex

@inproceedings{BUT189437,
  author="Ondřej {Olšák} and Jiří {Jaroš}",
  title="Techniques for Efficient Fourier Transform Computation in Ultrasound Simulations",
  booktitle="HPDC '24: Proceedings of the 33nd International Symposium on High-Performance Parallel and Distributed Computing",
  year="2024",
  pages="361--363",
  publisher="Association for Computing Machinery",
  address="New York",
  doi="10.1145/3625549.3658825",
  isbn="979-8-4007-0413-0",
  url="https://dl.acm.org/doi/10.1145/3625549.3658825"
}