Compressed Sensing Photoacoustic Imaging Reconstruction Using Elastic Net Approach

Abstract

Photoacoustic imaging involves reconstructing an estimation of the absorbed energy density distribution from measured ultrasound data. The reconstruction task based on incomplete and noisy experimental data is usually an ill-posed problem that requires regularization to obtain meaningful solutions. The purpose of the work is to propose an elastic network (EN) model to improve the quality of reconstructed photoacoustic images. To evaluate the performance of the proposed method, a series of numerical simulations and tissue-mimicking phantom experiments are performed. The experiment results indicate that, compared with the $L_1$ -norm and $L_2$ -normbased regularization methods with different numerical phantoms, Gaussian noise of 10-50 dB, and different regularization parameters, the EN method with $\alpha =0.5$ has better image quality, calculation speed, and antinoise ability.