Accelerated Intravascular Ultrasound Imaging using Deep Reinforcement Learning

Tristan S.W. Stevens, Nishith Chennakeshava, Frederik J. de Bruijn, Martin Pekař, Ruud J.G. van Sloun

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)
1 Downloads (Pure)

Abstract

Intravascular ultrasound (IVUS) offers a unique perspective in the treatment of vascular diseases by creating a sequence of ultrasound-slices acquired from within the vessel. However, unlike conventional hand-held ultrasound, the thin catheter only provides room for a small number of physical channels for signal transfer from a transducer-array at the tip. For continued improvement of image quality and frame rate, we present the use of deep reinforcement learning to deal with the current physical information bottleneck. Valuable inspiration has come from the field of magnetic resonance imaging (MRI), where learned acquisition schemes have brought significant acceleration in image acquisition at competing image quality. To efficiently accelerate IVUS imaging, we propose a framework that utilizes deep reinforcement learning for an optimal adaptive acquisition policy on a per-frame basis enabled by actor-critic methods and Gumbel top-K sampling.
Original languageEnglish
Title of host publication2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers
Pages1216-1220
Number of pages5
ISBN (Electronic)978-1-6654-0540-9
DOIs
Publication statusPublished - 27 Apr 2022
Event2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore, Singapore
Duration: 23 May 202227 May 2022
Conference number: 47
https://2022.ieeeicassp.org/

Conference

Conference2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Abbreviated titleICASSP 2022
Country/TerritorySingapore
CitySingapore
Period23/05/2227/05/22
Internet address

Keywords

  • Deep reinforcement learning
  • compressed sensing
  • intravascular ultrasound

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