Zero-shot low-dose CT denoising across variable schemes via strip-scanning diffusion models

Abstract

Artifacts and noise in low-dose CT (LDCT) may degrade image quality, potentially impacting subsequent diagnoses. In recent years, supervised image post-processing methods have been extensively studied for their effectiveness in noise reduction. However, clinical conditions often make it difficult to obtain paired normal-dose and low-dose CT images. Additionally, scanning protocols in clinical settings are diverse, necessitating different thickness or dose settings, which further complicates and increases the cost of low-dose data collection. These challenges limit the practical application and widespread adoption of supervised methods. This study introduces a novel end-to-end zero-shot strip-scanning diffusion model (SSDiff) that requires only a single model trained on normal-dose CT (NDCT) images to achieve LDCT image denoising across various scanning protocols with different slice thicknesses, doses, or devices. The sampling process employs a strip scanning strategy that combines overlapping strip information and input LDCT images to solve the maximum a posteriori problem to produce denoising results sequentially. We use only simple convolutional and attentional architectures and perform extensive experiments on three different datasets involving different doses, thicknesses, and devices; the results show that our method outperforms supervised methods in most cases, and visualization and blinded evaluations indicate that our method is very close to NDCT.