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

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Deep Proximal Unfolding For Image Recovery from Under-Sampled Channel Data in Intravascular Ultrasound

May 23, 2022 · 1 min read

Venue: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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

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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.

IVUS recon

Comparison of IVUS reconstruction methods: the under-sampled input, (a) UNet, (b) LISTA, (c) the proposed Unfolded Proximal Gradient network, and the ground truth.

@inproceedings{chennakeshava2022deep,
  title     = {Deep {{Proximal Unfolding For Image Recovery}}
               from {{Under-Sampled Channel Data}} in
               {{Intravascular Ultrasound}}},
  author    = {Chennakeshava, Nishith and
               Stevens, Tristan S.W. and
               de Bruijn, Frederik J. and Hancock, Andrew and
               Pekař, Martin and Eldar, Yonina C. and
               Mischi, Massimo and van Sloun, Ruud J.G.},
  booktitle = {{{ICASSP}} 2022 - 2022 {{IEEE International
               Conference}} on {{Acoustics}}, {{Speech}} and
               {{Signal Processing}} ({{ICASSP}})},
  year      = {2022},
  pages     = {1221--1225},
  doi       = {10.1109/ICASSP43922.2022.9746195},
}

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