Learning Sub-Sampling and Signal Recovery with Applications in Ultrasound Imaging

Limitations on bandwidth and power consumption impose strict bounds on data rates of diagnostic imaging systems. Consequently, the design of suitable (i.e. task- and data-aware) compression and reconstruction techniques has attracted considerable attention in recent years. Compressed sensing emerged as a popular framework for sparse signal reconstruction from a small set of compressed measurements. However, typical compressed sensing designs measure a (non)linearly weighted combination of all input signal elements, which poses practical challenges. These designs are also not necessarily task-optimal. In addition, real-time recovery is hampered by the iterative and time-consuming nature of sparse recovery algorithms. Recently, deep learning methods have shown promise for fast recovery from compressed measurements, but the design of adequate and practical sensing strategies remains a challenge. Here, we propose a deep learning solution, termed LASSY (LeArning Sub-Sampling and recoverY), that jointly learns a task-driven sub-sampling pattern and subsequent reconstruction model. The learned sub-sampling patterns are straightforwardly implementable, and based on the task at hand. LASSY's effectiveness is demonstrated in-silico for sparse signal recovery from partial Fourier measurements, and in-vivo for both anatomical-image and motion (Doppler) reconstruction from sub-sampled medical ultrasound imaging data.