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-2 | Engels |
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Titel | 2021 IEEE International Ultrasonics Symposium (IUS) |
Uitgeverij | Institute of Electrical and Electronics Engineers |
Aantal pagina's | 4 |
ISBN van elektronische versie | 978-1-6654-0355-9 |
DOI's | |
Status | Gepubliceerd - 15 nov. 2021 |
Evenement | 2021 IEEE International Ultrasonics Symposium, IUS 2021 - Virtual, Online, Xi'an, China Duur: 11 sep. 2011 → 16 sep. 2011 |
Congres
Congres | 2021 IEEE International Ultrasonics Symposium, IUS 2021 |
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Verkorte titel | IUS 2021 |
Land/Regio | China |
Stad | Xi'an |
Periode | 11/09/11 → 16/09/11 |