3D Carotid Artery Flow Imaging Using Compressive Sensing with a Spatial Coding Mask

A Simulation Study

Conference paper (2023)

Authors

Yuyang Hu Erasmus MC
D. Doğan Signal Processing Systems - EEMCS
Michael D. Brown Erasmus MC
Mahé Bulot Active Probe Group
Guillaume Ferin Active Probe Group
G.J.T. Leus Signal Processing Systems - EEMCS
P. Kruizinga Erasmus MC
Antonius F.W. Steen Erasmus MC
Johannes G. Bosch Erasmus MC
Research Group
Signal Processing Systems (EEMCS) (TU Delft)
Copyright: © 2023 Yuyang Hu, D. Doğan, Michael Brown, Mahé Bulot, Guillaume Ferin, G.J.T. Leus, P. Kruizinga, Antonius F.W. Steen, Johannes G. Bosch
DOI
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https://doi.org/10.1109/IUS51837.2023.10307110
Simulation Carotid artery Compressive imaging Matched filtering 3D image reconstruction
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Published Date

2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

Abstract

It has been previously demonstrated that applying an aberrating mask for 2D compressive imaging using a low number of sensors (elements) can significantly improve image resolution, as evaluated via the point spread function. Here we investigate the potential to apply a similar approach for 3D flow monitoring. We conducted a 3D k-Wave simulation using a 5x5 sensor array coupled to a physical coding mask, performing B-mode and power Doppler imaging on a 3D carotid artery flow model. An approximately three times smaller lateral PSF was achieved at the cost of increased background clutter level and slightly increased axial PSF. A better definition of the vessel border and finer flow speckle were observed in power Doppler imaging. Our results suggest that 3D compressive imaging using a very low sensor count of 25 with spatial coding mask has the potential to monitor 3D carotid artery flow.