UNAD: Universal Anatomy-Initialized Noise Distribution Learning Framework Towards Low-Dose CT Denoising

Abstract

Various deep-learning-based Low-dose Computed Tomography (LDCT) denoising methods have been proposed to address the issue of CT image noise, often relying on powerful network architecture and meticulously designed loss functions. However, these models are typically lacking efficient noise representations and prior anatomical knowledge, which can limit their overall performance. To address this issue, we introduce UNiversal Anatomy-initialized Noise Distribution Learning Framework (UNAD). The novelties of UNAD are in two aspects: 1) Distribution Regression Layer (DRL) restructures the output layer, enhancing noise prediction capability. 2) A simple yet efficient Predictive Pre-training Framework (PPT), tailored to the unique characteristics of CT images, is used to improve prior anatomical knowledge. We have achieved state-of-the-art results even without a powerful feature extractor like ViT. In addition, UNAD demonstrates remarkable universality, making it easy to incorporate into other LDCT denoising networks. The source code can be found in our official GitHub repository https://github.com/Nioolek/UNAD.