Noise Reduction for a Virtual Grid Using a Generative Adversarial Network in Breast X-ray Images.

In this study, we aimed to address the issue of noise amplification after scatter correction when using a virtual grid in breast X-ray images. To achieve this, we suggested an algorithm for estimating noise level and developed a noise reduction algorithm based on generative adversarial networks (GANs). Synthetic scatter in breast X-ray images were collected using Sizgraphy equipment and scatter correction was performed using dedicated software. After scatter correction, we determined the level of noise using noise-level function plots and trained a GAN using 42 noise combinations. Subsequently, we obtained the resulting images and quantitatively evaluated their quality by measuring the contrast-to-noise ratio (CNR), coefficient of variance (COV), and normalized noise-power spectrum (NNPS). The evaluation revealed an improvement in the CNR by approximately 2.80%, an enhancement in the COV by 12.50%, and an overall improvement in the NNPS across all frequency ranges. In conclusion, the application of our GAN-based noise reduction algorithm effectively reduced noise and demonstrated the acquisition of improved-quality breast X-ray images.