MSDiff: Multi-Scale Diffusion Model for Ultra-Sparse View CT Reconstruction

TL;DR

This paper introduces MSDiff, a multi-scale diffusion model that enhances ultra-sparse view CT reconstruction by focusing on global and local image features, significantly improving image quality with fewer projection angles.

Contribution

The paper presents a novel multi-scale diffusion model that integrates comprehensive and sparse sampling techniques for improved ultra-sparse view CT reconstruction.

Findings

Significant improvement in image quality under ultra-sparse angles.

Good generalization across various datasets.

Effective focus on global and local image features.

Abstract

Computed Tomography (CT) technology reduces radiation haz-ards to the human body through sparse sampling, but fewer sampling angles pose challenges for image reconstruction. Score-based generative models are widely used in sparse-view CT re-construction, performance diminishes significantly with a sharp reduction in projection angles. Therefore, we propose an ultra-sparse view CT reconstruction method utilizing multi-scale dif-fusion models (MSDiff), designed to concentrate on the global distribution of information and facilitate the reconstruction of sparse views with local image characteristics. Specifically, the proposed model ingeniously integrates information from both comprehensive sampling and selectively sparse sampling tech-niques. Through precise adjustments in diffusion model, it is capable of extracting diverse noise distribution, furthering the understanding of the overall structure of images, and aiding the fully sampled model in recovering image information more effec-tively. By leveraging the inherent correlations within the projec-tion data, we have designed an equidistant mask, enabling the model to focus its attention more effectively. Experimental re-sults demonstrated that the multi-scale model approach signifi-cantly improved the quality of image reconstruction under ultra-sparse angles, with good generalization across various datasets.