High-resolution and high-sensitivity blood flow estimation using deconvolution and optimization approaches with application to thyroid vascularization imaging

Hong Shen (1) , Chloé Barthélémy (2) , Elise Khoury (2) , Ilyess Zemmoura (2) , Jean-Pierre Remenieras (2) , Adrian Basarab (1, 3) , Denis Kouamé (1)

1 IRIT-MINDS - CoMputational imagINg anD viSion
2 iBraiN - Imaging, Brain & Neuropsychiatry
3 UT3 - Université Toulouse III - Paul Sabatier

In this paper, we address the problem of highresolution flow estimation in medical ultrasound images. Imaging methods based on ultrafast sequences associated with adaptive spatiotemporal SVD clutter filtering have recently improved blood flow detection. Herein, we investigate a new way of addressing the clutter filtering problem in order to obtain a highresolution flow estimation, through solving an inverse problem corresponding to both deconvolution and robust principal component analysis. Applied to tissue vascularization imaging via power Doppler images, the proposed method highlights finer details on experimental data compared to existing approaches.

Mots clés: Blood flow; Medical ultrasound; Sensitive Doppler; Robust PCA; Deconvolution; Optimization

Domaines: Traitement du signal et de l'image [eess.SP]; Imagerie médicale

Hong Shen, Chloé Barthélémy, Elise Khoury, Ilyess Zemmoura, Jean-Pierre Remenieras, et al.. High-resolution and high-sensitivity blood flow estimation using deconvolution and optimization approaches with application to thyroid vascularization imaging. IEEE International Ultrasonics Symposium (IUS 2019), Oct 2019, Glasgow, United Kingdom. pp.467-470, ⟨10.1109/ULTSYM.2019.8925840⟩. ⟨hal-02930117⟩