SDCNN: Self-Supervised Disentangled Convolutional Neural Network for Low-Dose CT Denoising

Low-dose computed tomography (LDCT) reduces radiation exposure but suffers from high noise, impacting image quality and diagnostic accuracy. Supervised learning has helped address this challenge but requires numerous paired datasets of LDCT and normal-dose CT (NDCT) images, which limits their clinical practice. This article proposes a novel self-supervised disentangled convolutional neural network (SDCNN) that can directly reconstruct high-quality CT images from LDCT data without the need for a clean reference. Unlike other methods that treat noise as a uniform entity, SDCNN disentangles LDCT images into noise-free images, signal-dependent noise, and signal-independent noise, aligning with the intrinsic principles of low-dose noise generation. To enhance the purity of disentanglement, we introduce the concept of combination and re-disentanglement to establish a training framework based on SDCNN. Additionally, we design self-supervised loss functions, including novel anisotropic total variation (TV) and distance loss functions, to improve the efficiency of the denoising process. The signal-guided attention (SGA) module effectively captures the relationship between signal-dependent noise and the signal across both spatial and channel dimensions. Experiments on clinical and animal data demonstrate that the proposed method performed better than all competing state-of-the-art (SOTA) self-supervised algorithms in noise and artifact removal. For example, compared to the self-supervised algorithms, SDCNN improves the multiscale structural similarity (MSSIM), peak signal-to-noise ratio (PSNR), and feature similarity (FSIM) by at least 2.26%, 1.20 dB, 1.23%, whereas, the gradient magnitude similarity deviation (GMSD) is reduced by at least 1.11% on the Mayo clinical data. The code is available athttps://github.com/YuhangLiu98/SDCNN.