W. Simson, M. Paschali, N. Navab, G. Zahnd Deep Learning Beamforming for Sub-Sampled Ultrasound Data IEEE International Ultrasonics Symposium (IUS), 2018 Japan (bib)
	In medical imaging tasks, such as cardiac imaging, ultrasound acquisition time is crucial, however traditional high-quality beamforming techniques are computationally expensive and their performance is hindered by sub-sampled data. To this end, we propose DeepFormer?, a method to reconstruct high quality ultrasound images in real-time on sub-sampled raw data by performing an end-to-end deep learning-based reconstruction. Results on an in vivo dataset of 19 participants show that DeepFormer? offers promising advantages over traditional processing of sub-sampled raw-ultrasound data and produces reconstructions that are both qualitatively and visually equivalent to fully-sampled DeepFormed? images.
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