  Emerging sonography techniques often imply increasing in the number of
transducer elements involved in the imaging process. Consequently, larger
amounts of data must be acquired and processed by the beamformer. The
significant growth in the amounts of data effects 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 manage to achieve such 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.
