Medical ultrasound image reconstruction using distributed compressive sampling

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.

[1]  Adrian Basarab,et al.  Frequency Domain Compressive Sampling for Ultrasound Imaging , 2012 .

[2]  R.G. Baraniuk,et al.  Distributed Compressed Sensing of Jointly Sparse Signals , 2005, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005..

[3]  Yonina C. Eldar,et al.  Compressed Beamforming in Ultrasound Imaging , 2012, IEEE Transactions on Signal Processing.

[4]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[5]  Denis Friboulet,et al.  Compressive sensing in medical ultrasound , 2012, 2012 IEEE International Ultrasonics Symposium.

[6]  Wotao Yin,et al.  Group sparse optimization by alternating direction method , 2013, Optics & Photonics - Optical Engineering + Applications.

[7]  Joel A. Tropp,et al.  Simultaneous sparse approximation via greedy pursuit , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[8]  Georg Schmitz,et al.  Compressed Sensing for Fast Image Acquisition in Pulse-Echo Ultrasound , 2012 .

[9]  Denis Friboulet,et al.  Pre-beamformed RF signal reconstruction in medical ultrasound using compressive sensing. , 2013, Ultrasonics.

[10]  Qiong Zhang,et al.  A measurement-domain adaptive beamforming approach for ultrasound instrument based on distributed compressed sensing: Initial development. , 2013, Ultrasonics.

[11]  Stephen P. Boyd,et al.  Enhancing Sparsity by Reweighted ℓ1 Minimization , 2007, 0711.1612.