Limited-View Photoacoustic Imaging Reconstruction Via High-quality Self-supervised Neural Representation

TL;DR

This paper presents a self-supervised neural network method called HIS for reconstructing high-quality photoacoustic images from limited-view sensor data, addressing the challenge of incomplete data in practical biomedical imaging.

Contribution

The study introduces a novel self-supervised neural representation approach that models photoacoustic images as continuous functions, enabling improved reconstruction from limited-view data.

Findings

HIS outperforms three common reconstruction methods in image quality.

The method effectively reconstructs images from sparse sensor data.

HIS demonstrates robustness in limited-view photoacoustic imaging.

Abstract

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.