Sparse-view Signal-domain Photoacoustic Tomography Reconstruction Method Based on Neural Representation

TL;DR

This paper introduces a neural network-based sparse reconstruction method for photoacoustic tomography that significantly reduces artifacts and improves image quality from limited data, potentially lowering system costs.

Contribution

The novel implicit neural representation approach effectively reconstructs high-quality images from sparse data, outperforming existing methods in simulation and experiments.

Findings

Outperforms existing methods in simulation and experimental data

Suppresses artifacts and avoids ill-posed problems under sparse sampling

Reconstructs images with higher SNR and CNR

Abstract

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.