Fully dense generative adversarial network for removing artifacts caused by microwave dielectric effect in thermoacoustic imaging

Microwave-induced thermoacoustic (TA) imaging (MTAI) combines pulsed microwave excitation and ultrasound detection to provide high contrast and spatial resolution images through dielectric contrast, which holds great promise for clinical applications. However, artifacts caused by microwave dielectric effect will seriously affect the accuracy of MTAI images that will hinder the clinical translation of MTAI. In this work, we propose a deep learning-based method fully dense generative adversarial network (FD-GAN) for removing artifacts caused by microwave dielectric effect in MTAI. FD-GAN adds the fully dense block to the generative adversarial network (GAN) based on the mutual confrontation between generator and discriminator, which enables it to learn both local and global features related to the removal of artifacts and generate high-quality images. The practical feasibility was tested in simulated, experimental data. The results demonstrate that FD-GAN can effectively remove the artifacts caused by the microwave dielectric effect, and shows superiority in denoising, background suppression, and improvement of image distortion. Our approach is expected to significantly improve the accuracy and quality of MTAI images, thereby enhancing the diagnostic accuracy of this innovative imaging technique.