Compressed sensing of ultrasound images: sampling of spatial and frequency domains

Céline Quinsac (1) , Adrian Basarab (1, 2) , Jean-Marc Girault (3) , Denis Kouamé (1)

1 IRIT-TCI - Traitement et Compréhension d’Images
2 UT3 - Université Toulouse III - Paul Sabatier
3 iBraiN - Imaging, Brain & Neuropsychiatry

This paper proposes a comparison between an established (used in magnetic resonance imaging) and a innovative compressed sensing (CS) approach, both adapted to ultrasound (US) imaging. Two undersampling patterns suited to US imaging were investigated in each approach on simulated and in vivo radio-frequency US images. Reconstructions of simulated and in vivo US images using CS show minimal information loss. The best strategy (minimising the errors of reconstruction) was a uniform random sampling in the two directions of the spatial RF US image associated with the reconstruction of its k-space.

Mots clés: compressed sensing; ultrasound imaging; reconstruction; k-space; image reconstruction; radio frequency

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

Céline Quinsac, Adrian Basarab, Jean-Marc Girault, Denis Kouamé. Compressed sensing of ultrasound images: sampling of spatial and frequency domains. IEEE Workshop on Signal Processing Systems (SiPS 2010) : “Bio-medical Signal Processing Systems”, Oct 2010, San Francisco, United States. pp.231--236, ⟨10.1109/SIPS.2010.5624793⟩. ⟨hal-03146736⟩