A deep learning approach to ultrasound image recovery
Compressed sensing (CS) has drawn many interest in the field ultrasound (US) image recovery. It has demonstrated promising results in the recovery of radio-frequency element raw-data [Liebgott et. al. ULTRAS13, Besson et. al. SPARS17]. The objective of such approaches is to recover the raw-data from undersampled random measurements. It is achieved by means of convex optimization or greedy methods which require a high number of iterations to converge and whose accuracy highly depends on hyper-parameters fine-tuning. Recently, deep-neural networks (DNN) has redefined the paradigm of signal recovery, leading to remarkable results for CS reconstruction of natural images [Mousavi et. al. ALBERTON15].