  Emerging sonography techniques often require increasing the number of
transducer elements involved in the imaging process. Consequently, larger
amounts of data must be acquired and processed. The significant growth in the
amounts of data affects both machinery size and power consumption. Within the
classical sampling framework, state of the art systems reduce processing rates
by exploiting the bandpass bandwidth of the detected signals. It has been
recently shown, that a much more significant sample-rate reduction may be
obtained, by treating ultrasound signals within the Finite Rate of Innovation
framework. These ideas follow the spirit of Xampling, which combines classic
methods from sampling theory with recent developments in Compressed Sensing.
Applying such low-rate sampling schemes to individual transducer elements,
which detect energy reflected from biological tissues, is limited by the noisy
nature of the signals. This often results in erroneous parameter extraction,
bringing forward the need to enhance the SNR of the low-rate samples. In our
work, we achieve SNR enhancement, by beamforming the sub-Nyquist samples
obtained from multiple elements. We refer to this process as "compressed
beamforming". Applying it to cardiac ultrasound data, we successfully image
macroscopic perturbations, while achieving a nearly eight-fold reduction in
sample-rate, compared to standard techniques.
