Application of $L_{1}-L_{2}$ Regularization in Sparse-View Photoacoustic Imaging Reconstruction

Photoacoustic imaging (PAI) is an advanced technique used to reconstruct the distribution of energy absorption in tissues, even when ultrasound signals are incomplete and noisy. However, the reconstruction process is challenging due to the ill-posed nature of the problem. In order to address this challenge, regularization techniques are employed to obtain a meaningful solution. This article focuses on the significance of utilizing
$L_{1}-L_{2}$
norm based on the difference of convex algorithm (DCA) in sparse photoacoustic image reconstruction. To assess the performance of
$L_{1}-L_{2}$
norm based on the DCA, a comparative test was conducted using three evaluation indicators. The sampling amount and noise level were controlled to effectively evaluate its effectiveness. The results from tissue phantom experiments demonstrated that the
$L_{1}-L_{2}$
norm based on the DCA method excelled in handling the reconstruction of highly noisy data with incomplete levels. Additionally, in a pig liver experiment, the
$L_{1}-L_{2}$
norm based on the DCA method was compared to other methods and found to be superior in reducing errors and ensuring stability. Importantly, the
$L_{1}-L_{2}$
norm based on the DCA method achieved similar image quality with a sampling number of 40, while other methods required a higher sampling number of 80. In scenarios with significant noise and the number of low sampling, the
$L_{1}-L_{2}$
norm based on the DCA method showcases its capability to deliver satisfactory reconstruction results. This discovery holds significant potential for enhancing sparse sampling photoacoustic tomography algorithms, and it offers valuable insights for future biomedical application development.