  Photoacoustic tomography is a hybrid biomedical technology, which combines
the advantages of acoustic and optical imaging. However, for the conventional
image reconstruction method, the image quality is affected obviously by
artifacts under the condition of sparse sampling. in this paper, a novel
model-based sparse reconstruction method via implicit neural representation was
proposed for improving the image quality reconstructed from sparse data.
Specially, the initial acoustic pressure distribution was modeled as a
continuous function of spatial coordinates, and parameterized by a multi-layer
perceptron. The weights of multi-layer perceptron were determined by training
the network in self-supervised manner. And the total variation regularization
term was used to offer the prior knowledge. We compared our result with some
ablation studies, and the results show that out method outperforms existing
methods on simulation and experimental data. Under the sparse sampling
condition, our method can suppress the artifacts and avoid the ill-posed
problem effectively, which reconstruct images with higher signal-to-noise ratio
and contrast-to-noise ratio than traditional methods. The high-quality results
for sparse data make the proposed method hold the potential for further
decreasing the hardware cost of photoacoustic tomography system.
