Samenvatting
Aperture Domain Model Image REconstruction (ADMIRE) is an advanced ultrasound beamforming method that uses a model-based approach to suppress sources of acoustic clutter and improve ultrasound image quality. However, although effective, ADMIRE requires solving an inverse problem that is ill-posed, which means that there are infinitely many solutions that can have different impacts on image quality. Currently, linear regression with elastic-net regularization is used to obtain a solution, but there are potentially better methods for performing model fitting. Therefore, in this work, we propose using a deep neural network sparse encoder for performing the model fits of ADMIRE. In particular, we unfold the iterations of the iterative shrinkage and thresholding algorithm (ISTA) as a feedforward neural network and train it using different training schemes to perform sparse coding. Test results using both simulated and in vivo data demonstrate that ADMIRE using a deep neural network sparse encoder has the potential to outperform conventional ADMIRE in terms of ultrasound image quality while still preserving the model-based intuition of ADMIRE.
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 |
Bibliografische nota
Publisher Copyright:© 2021 IEEE.
Financiering
The authors would like to thank the staff of the AC-CRE computing resource. This work was supported by NIH grants R01HL156034, R01EB020040, and S10OD016216-01, NAVSEA grant N0002419C4302, and NSF award IIS-1750994.