Medical ultrasound image reconstruction using distributed compressive sampling

Adrian Basarab (1, 2) , Hervé Liebgott (3) , Olivier Bernard (4) , Denis Friboulet (4) , Denis Kouamé (1)

1 IRIT-TCI - Traitement et Compréhension d’Images
2 UT3 - Université Toulouse III - Paul Sabatier
3 Imagerie Ultrasonore
4 Images et Modèles

This paper investigates ultrasound (US) radiofrequency (RF) signal recovery using the distributed compressed sampling framework. The “correlation” between the RF signals forming a RF image is exploited by assuming that they have the same sparse support in the 1D Fourier transform, with different coefficient values. The method is evaluated using an experimental US image. The results obtained are shown to improve a previously proposed recovery method, where the correlation between RF signals was taken into account by assuming the 2D Fourier transform of the RF image sparse.

Mots clés: Compressive sampling; Fourier transform; Radiofrequency signals; Ultrasound imaging; Jointly sparse signal

Domaines: Synthèse d'image et réalité virtuelle [cs.GR]; Traitement du signal et de l'image [eess.SP]; Traitement des images [eess.IV]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]

Adrian Basarab, Hervé Liebgott, Olivier Bernard, Denis Friboulet, Denis Kouamé. Medical ultrasound image reconstruction using distributed compressive sampling. IEEE International Symposium on Biomedical Imaging: From Nano to Macro - ISBI 2013, Apr 2013, San Francisco, United States. pp. 628-631. ⟨hal-01150347⟩