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.
AU - Hancock, Anew
N1 - Publisher Copyright: © 2022 IEEE
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
UR - http://www.scopus.com/inward/record.url?scp=85131239794&partnerID=8YFLogxK
U2 - https://doi.org/10.1109/ICASSP43922.2022.9746195
DO - https://doi.org/10.1109/ICASSP43922.2022.9746195
M3 - منشور من مؤتمر
SN - 9781665405416
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1221
EP - 1225
BT - 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
T2 - 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Y2 - 23 May 2022 through 27 May 2022
ER -
