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Compressive Imaging with Spatial Coding Masks on Low Number of Elements

An Emulation Study

Conference Paper (2022)
Author(s)

Yuyang Hu (Erasmus MC)

Michael D. Brown (Erasmus MC)

D. Doğan (TU Delft - Signal Processing Systems)

G.J.T. Leus (TU Delft - Signal Processing Systems)

P. Kruizinga (Erasmus MC)

Antonius F.W. van der Steen (Erasmus MC)

J.G. Bosch (Erasmus MC)

Research Group
Signal Processing Systems
Copyright
© 2022 Yuyang Hu, Michael Brown, D. Doğan, G.J.T. Leus, P. Kruizinga, Antonius F.W. Van Der Steen, Johannes G. Bosch
DOI related publication
https://doi.org/10.1109/IUS54386.2022.9957699
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 Yuyang Hu, Michael Brown, D. Doğan, G.J.T. Leus, P. Kruizinga, Antonius F.W. Van Der Steen, Johannes G. Bosch
Research Group
Signal Processing Systems
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
ISBN (print)
978-1-6654-7813-7
ISBN (electronic)
978-1-6654-6657-8
Reuse Rights

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Abstract

We intend to develop an ultrasound compressive imaging device to perform carotid artery (CA) function and flow monitoring/imaging by using just a few single element transducers equipped with spatial coding masks. The spatially unique impulse responses can be exploited in compressive reconstructions. To explore the potential of different configurations, in this study we emulated such a device using a linear array system. We combined its elements with individual digital delays into a small number of groups. The results suggest our spatial coding mask approach based on reconstructions regularized with a least squares method has potential for CA monitoring with only 10 to 12 sensors.

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