Ultrasound (US) is a widely used medical imaging modality mostly because of its non-invasive and real-time characteristics. Recent advances in US imaging (e.g. ultrafast imaging, 3D imaging, elastography, functional imaging etc.) gave rise to a crucial challenge: dealing with the huge amount of data that has to be transferred and processed in real-time. To address this problem, the LTS5 is focusing on two main aspects: 1) Maximizing the image quality for a given amount of data using advanced image reconstruction methods 2) Minimizing the data-rate to reach a given image q

US devices generate a set of signals that are carried from a transducer probe to a computer for further processing in order to obtain images. Those signals are transmitted between both ends through a set of cables, making up a high capacity data transmission channel. In order to achieve a portable US device, it will be required to transfer the data through a much lower capacity channel. To reduce the data - rate, deep/convolutional neural networks are used for this purpose in this master thesis, showing that it is possible to reduce remarkably the data rates generated by those devices while keeping a high quality in the final reconstructed ultrasound images.