Model-based Deep Learning on Ultrasound Channel Data for Fast Ultrasound Localization Microscopy

Jihwan Youn, Ben Luijten, Mikkel Schou, Matthias Bo Stuart, Yonina C. Eldar, Ruud J.G. van Sloun, Jørgen Arendt Jensen

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademic

5 Citaten (Scopus)
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Samenvatting

Ultrasound localization microscopy (ULM) can break the diffraction limit of ultrasound imaging. However, a long data acquisition time is often required due to the use of low concentrations of microbubbles (MBs) for high localization accuracy. Lately, deep learning-based methods that can robustly localize high concentrations of microbubbles (MBs) have been proposed to overcome this constraint. In particular, deep unfolded ULM has shown promising results with a few parameters by using a sparsity prior. In this work, deep unfolded ULM is further extended to perform beamforming as well as MB localization. The proposed network learns data-dependent beamforming weights that are optimal for deep unfolded ULM to locate MBs. The images beamformed by the network were sharper than delay-and-sum beamformed images. In a simulated test set at an MB density of 3.84 mm −1 , the proposed network reconstructed 87 % of MBs with the precision of 0.99 while achieving comparable localization accuracy to deep unfolded ULM, when centroid detection and deep unfolded ULM reconstructed 42 % and 67 % of MBs with the precision of 0.75 and 0.99, respectively.
Originele taal-2Engels
Titel2021 IEEE International Ultrasonics Symposium (IUS)
UitgeverijInstitute of Electrical and Electronics Engineers
Aantal pagina's4
ISBN van elektronische versie978-1-6654-0355-9
DOI's
StatusGepubliceerd - 15 nov. 2021
Evenement2021 IEEE International Ultrasonics Symposium, IUS 2021 - Virtual, Online, Xi'an, China
Duur: 11 sep. 201116 sep. 2011

Congres

Congres2021 IEEE International Ultrasonics Symposium, IUS 2021
Verkorte titelIUS 2021
Land/RegioChina
StadXi'an
Periode11/09/1116/09/11

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