Total Variation-Based Limited-View Photoacoustic Reconstruction Method with Segmentation-Based Regularization

摘要

In Photoacoustic tomography (PAT), the reconstruction of limited-view scanning is a difficult point. The incompleteness of data will cause serious artifacts in the reconstructed images. The existing reconstruction algorithms failed to have ideal performance in terms of limited-view scanning. In this paper, we incorporate segmentation based regularization into a total variation-based photoacoustic reconstruction algorithm in order to improve the quality of the reconstructed results for limited-view scanning. Variable splitting and a Barzilai-Borwein based method are used to solve the optimization problem. More precise segmentation results can be acquired by using the compensation method during iteration. The proposed algorithm is validated by series of numerical simulations and in vitro experiments for both straight-line scanning and limited-angle circular scanning. Both simulation and experiment results show that the proposed algorithm is superior to the traditional TV-based algorithms which can effectively suppress artifacts caused by deficiencies in the sampling data, reduce background noise, and enhance the image edges.