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Joint Blind Deconvolution and Robust Principal Component Analysis for Blood Flow Estimation in Medical Ultrasound Imaging

Abstract : This article addresses the problem of high-resolution Doppler blood flow estimation from an ultrafast sequence of ultrasound images. Formulating the separation of clutter and blood components as an inverse problem has been shown in the literature to be a good alternative to spatio-temporal singular value decomposition (SVD)-based clutter filtering. In particular, a deconvolution step has recently been embedded in such a problem to mitigate the influence of the point spread function (PSF) of the imaging system. Deconvolution was shown in this context to improve the accuracy of the blood flow reconstruction. However, the PSF needs to be measured experimentally, and measuring it requires nontrivial experimental setups. To overcome this limitation, we propose herein a blind deconvolution method able to estimate both the blood component and the PSF from Doppler data. Numerical experiments conducted on simulated and in vivo data demonstrate qualitatively and quantitatively the effectiveness of the proposed approach in comparison with the previous method based on experimentally measured PSF and two other state-of-the-art approaches.
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https://hal.archives-ouvertes.fr/hal-03102546
Contributor : Duong-Hung Pham <>
Submitted on : Friday, January 8, 2021 - 10:09:09 AM
Last modification on : Wednesday, June 9, 2021 - 10:00:34 AM
Long-term archiving on: : Friday, April 9, 2021 - 6:29:43 PM

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Duong-Hung Pham, Adrian Basarab, Ilyess Zemmoura, Jean-Pierre Remenieras, Denis Kouamé. Joint Blind Deconvolution and Robust Principal Component Analysis for Blood Flow Estimation in Medical Ultrasound Imaging. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, Institute of Electrical and Electronics Engineers, 2021, 68 (4), pp.969 - 978. ⟨10.1109/TUFFC.2020.3027956⟩. ⟨hal-03102546⟩

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