Compressed sensing in photoacoustic tomography with in vivo experiments

Abstract

The data acquisition speed in photoacoustic computed tomography (PACT) is limited by the laser repetition rate and the number of parallel ultrasound detecting channels. Reconstructing PACT image with a less number of measurements can effectively accelerate the data acquisition and reduce the system cost. Recently emerged Compressed Sensing (CS) theory enables us to reconstruct a compressible image with a small number of projections. This paper adopts the CS theory for reconstruction in PACT. The idea is implemented as a non-linear conjugate gradient descent algorithm and tested with phantom and in vivo experiments.