Publication detail

Acceleration of Ultrasound Neurostimulation Using Mixed-Precision Arithmetic

JAROŠ, J. DUCHOŇ, R.

Original Title

Acceleration of Ultrasound Neurostimulation Using Mixed-Precision Arithmetic

Type

article in a collection out of WoS and Scopus

Language

English

Original Abstract

Ultrasound neurostimulation, a technique that modulates the brain's electrical activity, has emerged as a significant secondary treatment option for cases resistant to pharmacological interventions. The therapy is achievable through the application of a three-dimensional steerable ultrasound, directed by patient-specific stimulation plans. These plans are meticulously crafted through full-wave ultrasound propagation simulations. Nonetheless, the computational intensity required for calculating these plans poses a significant challenge, often reaching the memory capacities of contemporary graphics processing units (GPUs). By representing material properties and k-space operators more efficiently, we achieved a 22% reduction in precision GPU memory usage, while accelerating calculations by 8.5%. This optimization introduced an error that reduced focal pressure by 0.5% without any focus movement, values that are clinically acceptable.

Keywords

GPU, Nvidia, CUDA, k-Wave, Acceleration, Ultrasound, Acoustic waves, Neurostimulation, Mixed precision

Authors

JAROŠ, J.; DUCHOŇ, R.

Released

30. 8. 2024

Publisher

Association for Computing Machinery

Location

New York

ISBN

979-8-4007-0413-0

Book

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

Pages from

370

Pages to

372

Pages count

3

URL

https://www.fit.vut.cz/research/publication/13194/

BibTex

@inproceedings{BUT189462,
  author="Jiří {Jaroš} and Radek {Duchoň}",
  title="Acceleration of Ultrasound Neurostimulation Using Mixed-Precision Arithmetic",
  booktitle="HPDC '24: Proceedings of the 33rd International Symposium on High-Performance Parallel and Distributed Computing",
  year="2024",
  pages="370--372",
  publisher="Association for Computing Machinery",
  address="New York",
  doi="10.1145/3625549.3658823",
  isbn="979-8-4007-0413-0",
  url="https://www.fit.vut.cz/research/publication/13194/"
}