Distributed compressed sensing for photo-acoustic imaging

Photo-Acoustic Tomography (PAT) combines ultrasound resolution and penetration with endogenous optical contrast of tissue. Real-time PAT imaging is limited by the number of parallel data acquisition channels and pulse repetition rate of the laser. Typical photoacoustic signals afford sparse representation. Additionally, PAT transducer configurations exhibit significant intra- and inter- signal correlation. In this work, we formulate photoacoustic signal recovery in the Distributed Compressed Sensing (DCS) framework to exploit this correlation. Reconstruction using the proposed method achieves better image quality than compressed sensing with significantly fewer samples. Through our results, we demonstrate that DCS has the potential to achieve real-time PAT imaging.