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

doi:10.1109/ICASSP43922.2022.9746195

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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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 language	English
Title of host publication	2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers
Pages	1221-1225
Number of pages	5
ISBN (Electronic)	978-1-6654-0540-9
DOIs	https://doi.org/10.1109/ICASSP43922.2022.9746195
Publication status	Published - 27 Apr 2022
Event	2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore, Singapore Duration: 23 May 2022 → 27 May 2022 Conference number: 47 https://2022.ieeeicassp.org/

Conference

Conference	2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Abbreviated title	ICASSP 2022
Country/Territory	Singapore
City	Singapore
Period	23/05/22 → 27/05/22
Internet address	https://2022.ieeeicassp.org/

Keywords

AI
Denoising
IVUS
Model Based Neural Network

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10.1109/ICASSP43922.2022.9746195

Cite this

Chennakeshava, N., Stevens, T. S. W., de Bruijn, F. J., Hancock, A., Pekař, M., Eldar, Y. C., Mischi, M., & van Sloun, R. J. G. (2022). Deep Proximal Unfolding For Image Recovery from Under-Sampled Channel Data in Intravascular Ultrasound. In 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings (pp. 1221-1225). Article 9746195 Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICASSP43922.2022.9746195

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title = "Deep Proximal Unfolding For Image Recovery from Under-Sampled Channel Data in Intravascular Ultrasound",

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.",

keywords = "AI, Denoising, IVUS, Model Based Neural Network",

author = "Nishith Chennakeshava and Stevens, {Tristan S.W.} and {de Bruijn}, {Frederik J.} and Andrew Hancock and Martin Peka{\v r} and Eldar, {Yonina C.} and Massimo Mischi and {van Sloun}, {Ruud J.G.}",

year = "2022",

month = apr,

day = "27",

doi = "10.1109/ICASSP43922.2022.9746195",

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booktitle = "2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers",

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note = "2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022, ICASSP 2022 ; Conference date: 23-05-2022 Through 27-05-2022",

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}

Chennakeshava, N , Stevens, TSW, de Bruijn, FJ, Hancock, A, Pekař, M, Eldar, YC, Mischi, M & van Sloun, RJG 2022, Deep Proximal Unfolding For Image Recovery from Under-Sampled Channel Data in Intravascular Ultrasound. in 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings., 9746195, Institute of Electrical and Electronics Engineers, pp. 1221-1225, 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022, Singapore, Singapore, 23/05/22. https://doi.org/10.1109/ICASSP43922.2022.9746195

Deep Proximal Unfolding For Image Recovery from Under-Sampled Channel Data in Intravascular Ultrasound. / Chennakeshava, Nishith ; Stevens, Tristan S.W.; de Bruijn, Frederik J. et al.
2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. Institute of Electrical and Electronics Engineers, 2022. p. 1221-1225 9746195.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Deep Proximal Unfolding For Image Recovery from Under-Sampled Channel Data in Intravascular Ultrasound

AU - Chennakeshava, Nishith

AU - Stevens, Tristan S.W.

AU - de Bruijn, Frederik J.

AU - Hancock, Andrew

AU - Pekař, Martin

AU - Eldar, Yonina C.

AU - Mischi, Massimo

AU - van Sloun, Ruud J.G.

N1 - Conference code: 47

PY - 2022/4/27

Y1 - 2022/4/27

N2 - 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.

AB - 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.

KW - AI

KW - Denoising

KW - IVUS

KW - Model Based Neural Network

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U2 - 10.1109/ICASSP43922.2022.9746195

DO - 10.1109/ICASSP43922.2022.9746195

M3 - Conference contribution

SP - 1221

EP - 1225

BT - 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings

PB - Institute of Electrical and Electronics Engineers

T2 - 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022

Y2 - 23 May 2022 through 27 May 2022

ER -

Chennakeshava N , Stevens TSW, de Bruijn FJ, Hancock A, Pekař M, Eldar YC et al. Deep Proximal Unfolding For Image Recovery from Under-Sampled Channel Data in Intravascular Ultrasound. In 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. Institute of Electrical and Electronics Engineers. 2022. p. 1221-1225. 9746195 doi: 10.1109/ICASSP43922.2022.9746195

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

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