  Photoacoustic imaging (PAI) is a non-invasive imaging modality that detects
the ultrasound signal generated from tissue with light excitation.
Photoacoustic computed tomography (PACT) uses unfocused large-area light to
illuminate the target with ultrasound transducer array for PA signal detection.
Limited-view issue could cause a low-quality image in PACT due to the
limitation of geometric condition. The model-based method is used to resolve
this problem, which contains different regularization. To adapt fast and
high-quality reconstruction of limited-view PA data, in this paper, a
model-based method that combines the mathematical variational model with deep
learning is proposed to speed up and regularize the unrolled procedure of
reconstruction. A deep neural network is designed to adapt the step of the
gradient updated term of data consistency in the gradient descent procedure,
which can obtain a high-quality PA image only with a few iterations. Note that
all parameters and priors are automatically learned during the offline training
stage. In experiments, we show that this method outperforms the other methods
with half-view (180 degrees) simulation and real data. The comparison of
different model-based methods show that our proposed scheme has superior
performances (over 0.05 for SSIM) with same iteration (3 times) steps.
Furthermore, an unseen data is used to validate the generalization of different
methods. Finally, we find that our method obtains superior results (0.94 value
of SSIM for in vivo) with a high robustness and accelerated reconstruction.
