Performing Aperture Domain Model Image REconstruction (ADMIRE) Using a Deep Neural Network Sparse Encoder

Christopher Khan, Ruud J.G. van Sloun, Brett Byram

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

3 Citaten (Scopus)

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-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

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.

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