  Photoacoustic (PA) computed tomography (PACT) shows great potentials in
various preclinical and clinical applications. A great number of measurements
are the premise that obtains a high-quality image, which implies a low imaging
rate or a high system cost. The artifacts or sidelobes could pollute the image
if we decrease the number of measured channels or limit the detected view. In
this paper, a novel compressed sensing method for PACT using an untrained
neural network is proposed, which decreases half number of the measured
channels and recoveries enough details. This method uses a neural network to
reconstruct without the requirement for any additional learning based on the
deep image prior. The model can reconstruct the image only using a few
detections with gradient descent. Our method can cooperate with other existing
regularization, and further improve the quality. In addition, we introduce a
shape prior to easily converge the model to the image. We verify the
feasibility of untrained network based compressed sensing in PA image
reconstruction, and compare this method with a conventional method using total
variation minimization. The experimental results show that our proposed method
outperforms 32.72% (SSIM) with the traditional compressed sensing method in the
same regularization. It could dramatically reduce the requirement for the
number of transducers, by sparsely sampling the raw PA data, and improve the
quality of PA image significantly.
