Time Series Forecasting for Sparse Ring-shaped Array Photoacoustic Imaging Reconstruction

Abstract

Photoacoustic computed tomography (PACT), which provides high optical absorption contrast and deep acoustic penetration, plays an important role in non-invasive biomedical imaging area. As the decrease of array elements, the reconstructed image suffers from severe artifacts. Recent studies utilize deep learning methods to improve the imaging quality of PACT based on image network design, but few were reported with raw data. To address this issue, this paper proposes a Wave to Wave Convolution Gate Recurrent-Net (WWCG-Net) to reconstruct photoacoustic image based on time series acoustic signal prediction. Simulation and experiment results show the superiority of our method compared with linear interpolation (LI) and eXtreme Gradient Boosting (XGBoost) in term of suppress artifact and improve resolution.