**4. Conclusion and future developments**

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 image pixel.

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 models depending on the final application requirements.

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

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 Strategies for Hardware Reduction on the Design of Portable Ultrasound Imaging Systems 25

frame rate point of view. So by using a simple graphics card equipped with NVIDIA CUDA technology, rates that go up to 200 images per second were obtained depending on the image size chosen. Therefore, this solution allows the development of high quality imaging systems with low requirements and excellent capabilities in a compact architecture.
