The Gated Recurrent Conditional Generative Adversarial Network (GRC-GAN): application to denoising of low-dose CT images - Almeida, Mateus Baltazar de; Pereira, Luis F. Alves; Ren, Tsang Ing; Cavalcanti, George D. C.; Sijbers, Jan (Anais da Conference on Graphics, Patterns and Images (SIBGRAPI), 2021)
The ionizing radiation that propagates through the human body at Computed Tomography (CT) exams is known to be carcinogenic. For this reason, the development of methods for image reconstruction that operate with reduced radiation doses is essential. If we reduce the electrical current in the electrically powered X-ray tubes of CT scanners, the amount of radiation that passes through the human body during a CT exam is reduced. However, significant image noise emerges in the reconstructed CT slices if standard reconstruction methods are applied. To estimate routine-dose CT images from low-dose CT images and thus reduce noise, the Conditional Generative Adversarial Network (cGAN) was recently proposed in the literature. In this work, we introduce the Gated Recurrent Conditional Generative Adversarial Network (GRC-GAN) that is based on the usage of network gates to learn the specific regions of the input image to be updated using the cGAN denoising operation. Moreover, the GRC-GAN is executed recurrently in multiple time steps. At each time step, different parts of the input image are denoised. As a result, our GRC-GAN better focus on the denoise criterium than the regular cGAN in the LoDoPaB-CT benchmark.