Feature Aggregation in Perceptual Loss for Ultra Low-Dose (ULD) CT Denoising

Abstract

Lung cancer CT screening programs are continuously reducing patient exposure to radiation at the expense of image quality. State-of-the-art denoising algorithms are instrumental in preserving the diagnostic value of these images. In this work, a novel neural denoising scheme is proposed for ULD chest CT. The proposed method aggregates multi-scale features that provide rich information for the computation of a perceptive loss. The loss is further optimized for chest CT data by using denoising auto-encoders on real CT images to build the feature extracting network instead of using an existing network trained on natural images. The proposed method was validated on co-registered pairs of real ULD and normal dose scans and compared favorably with published state-of-the-art denoising$ networks both qualitatively and quantitatively.