Convergent–Diffusion Denoising Model for multi-scenario CT Image Reconstruction

Abstract

A generic and versatile CT Image Reconstruction (CTIR) scheme can efficiently mitigate imaging noise resulting from inherent physical limitations, substantially bolstering the dependability of CT imaging diagnostics across a wider spectrum of patient cases. Current CTIR techniques often concentrate on distinct areas such as Low-Dose CT denoising (LDCTD), Sparse-View CT reconstruction (SVCTR), and Metal Artifact Reduction (MAR). Nevertheless, due to the intricate nature of multi-scenario CTIR, these techniques frequently narrow their focus to specific tasks, resulting in limited generalization capabilities for diverse scenarios. We propose a novel Convergent–Diffusion Denoising Model (CDDM) for multi-scenario CTIR, which utilizes a stepwise denoising process to converge toward an imaging-noise-free image with high generalization. CDDM uses a diffusion-based process based on a priori decay distribution to steadily correct imaging noise, thus avoiding the overfitting of individual samples. Within CDDM, a domain-correlated sampling network (DS-Net) provides an innovative sinogram-guided noise prediction scheme to leverage both image and sinogram (i.e., dual-domain) information. DS-Net analyzes the correlation of the dual-domain representations for sampling the noise distribution, introducing sinogram semantics to avoid secondary artifacts. Experimental results validate the practical applicability of our scheme across various CTIR scenarios, including LDCTD, MAR, and SVCTR, with the support of sinogram knowledge.