  In practical applications within the human body, it is often challenging to
fully encompass the target tissue or organ, necessitating the use of
limited-view arrays, which can lead to the loss of crucial information.
Addressing the reconstruction of photoacoustic sensor signals in limited-view
detection spaces has become a focal point of current research. In this study,
we introduce a self-supervised network termed HIgh-quality Self-supervised
neural representation (HIS), which tackles the inverse problem of photoacoustic
imaging to reconstruct high-quality photoacoustic images from sensor data
acquired under limited viewpoints. We regard the desired reconstructed
photoacoustic image as an implicit continuous function in 2D image space,
viewing the pixels of the image as sparse discrete samples. The HIS's objective
is to learn the continuous function from limited observations by utilizing a
fully connected neural network combined with Fourier feature position encoding.
By simply minimizing the error between the network's predicted sensor data and
the actual sensor data, HIS is trained to represent the observed continuous
model. The results indicate that the proposed HIS model offers superior image
reconstruction quality compared to three commonly used methods for
photoacoustic image reconstruction.
