DEEP PROXIMAL UNFOLDING FOR IMAGE RECOVERY FROM UNDER-SAMPLED CHANNEL DATA IN INTRAVASCULAR ULTRASOUND

Nishith Chennakeshava, Tristan S.W. Stevens, Frederik J. de Bruijn, Andrew Hancock, Martin Pekař, Yonina C. Eldar, Massimo Mischi, Ruud J.G. van Sloun, Anew Hancock

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

Abstract

Intravascular UltraSound (IVUS) is a key tool in guiding the treatment and diagnosis of various coronary heart diseases. However, due to its nature IVUS is a very challenging modality to interpret, and suffers from a severely restricted data transfer rate. This forces a trade-off between temporal and spatial resolution. Here, we propose a model-based deep learning solution that aims to reconstruct images from data that has been beamformed by under-sampling the number of channels by a factor of 4. By exploiting the physics based measurement model, we achieve better performance and consistency in our predictions when compared to benchmark models. This lowers the computational load on existing hardware and enables in exploring our ability to run multiple visualisation modalities simultaneously, without a loss of temporal resolution.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
Pages1221-1225
Number of pages5
ISBN (Electronic)9781665405409
DOIs
StatePublished - 27 Apr 2022
Event47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore
Duration: 23 May 202227 May 2022

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2022-May

Conference

Conference47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Country/TerritorySingapore
CityVirtual, Online
Period23/05/2227/05/22

Keywords

  • AI
  • Denoising
  • IVUS
  • Model Based Neural Network

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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