A bridged denoising convolutional neural network (BD-CNN) for photon-counting CT

Abstract

Traditional monochromatic CT scanners have served as one of the most critical medical diagnostic instruments. Recently, the development of photon-counting CT brings spectral information to the otherwise black-and-white CT images, allowing the separation of materials. However, noise from a range of sources still degrades the overall image quality. Inspired by the successes of deep learning implementations in image processing, a bridged denoising convolutional neural network (BD-CNN) is proposed, aiming to reduce the noise presented in the image and enhance the overall image quality quantified by PSNR, RMSE, and SSIM. The BD-CNN is trained with a simulated labeled dataset of photon-counting CT. The results demonstrate that the denoising network improves the overall quality of the images and the noise presented in the original noisy images is decreased significantly. Compared to iterative denoising algorithms (Median filtering, BM3D), the proposed method not only achieved better results measured by the metrics, but it also demonstrates better consistency in image denoising. In conclusion, the proposed BD-CNN is a promising deep learning architecture for image denoising and can be further improved with minor adjustments. Further studies are needed for a more suitable loss function for deep learning in medical imaging and different image quality metrics optimized for comparing diagnostic value of medical images.