**Acknowledgments**

24 Breakthroughs in Ultrasound Imaging

266 Advancements and Breakthroughs in Ultrasound Imaging

to fast computation.

image pixel.

**4. Conclusion and future developments**

models depending on the final application requirements.

**Figure 18.** Images per second achieved using GPU for different image sizes for 2R-SAFT and kA-SAFT

pixels, the GPU is able to get 135 images per second, which is in nearly to the acquisition system's rate. The evidence here is that we are using a smaller dataset than that obtained with TFM method but preserving the image quality with all GPU cores completely dedicated

This work has presented how the use of coarray paradigm makes possible the design of ultrasonic imaging systems with reduced hardware requirements. The system is divided into two subsystems, hardware and software respectively. The first one is focused on the development of the data acquisition system, whose design is done analysing the compromise between parallel electronic resources and acquisition time. The second one exploits GPU technology to implement the beamformer via software, compensating the emission and reception distances to each image point, providing the maximum possible quality at each

Two solutions, based on the availability of instrumentation in the market, are presented attending to this design following the minimum redundancy coarray model. In one case, it has been emphasized the miniaturization of the hardware (with only two channels in reception), and in the second case the focus has been the reduction of the acquisition time at the expense of increasing and parallelize reception channels (up to a maximum of 32). From the point of view of image quality, both beamforming techniques present similar results. Consequently it is possible to adapt the design of our system to several implementation

The problems associated to the low level of the transmitted signals and the signal losses through the material have been analysed. As a solution, we have introduced pulse compression techniques in order to increase the signal to noise ratio. In addition, we have studied the implementation cost of this technique and it has been compared with the TFM

Finally, we have made a detailed description of the beamforming process in GPU and it has been quantified the advantage of using the GPU as a processing tool from the image

technique (based on the FMA capture), verifying that the results are very similar.

This work has been supported by the Spanish Ministry of Science and Competitiveness under the project DPI2010-19376.
