  Photoacoustic tomography seeks to reconstruct an acoustic initial pressure
distribution from the measurement of the ultrasound waveforms. Conventional
methods assume a-prior knowledge of the sound speed distribution, which
practically is unknown. One way to circumvent the issue is to simultaneously
reconstruct both the acoustic initial pressure and speed. In this article, we
develop a novel data-driven method that integrates an advanced deep neural
network through model-based iteration. The image of the initial pressure is
significantly improved in our numerical simulation.
