A Diffusion-Based Generative Prior Approach to Sparse-view Computed Tomography

TL;DR

This paper introduces a diffusion-based deep generative prior method for reconstructing CT images from sparse-view data, combining model-based optimization with neural generative models to improve image quality.

Contribution

It proposes modifications to diffusion-based generative models and iterative algorithms within the Deep Generative Prior framework for better sparse-view CT reconstruction.

Findings

Promising results under highly sparse geometries

Enhanced image reconstruction quality

Potential for further improvements in model and algorithm design

Abstract

The reconstruction of X-rays CT images from sparse or limited-angle geometries is a highly challenging task. The lack of data typically results in artifacts in the reconstructed image and may even lead to object distortions. For this reason, the use of deep generative models in this context has great interest and potential success. In the Deep Generative Prior (DGP) framework, the use of diffusion-based generative models is combined with an iterative optimization algorithm for the reconstruction of CT images from sinograms acquired under sparse geometries, to maintain the explainability of a model-based approach while introducing the generative power of a neural network. There are therefore several aspects that can be further investigated within these frameworks to improve reconstruction quality, such as image generation, the model, and the iterative algorithm used to solve the minimization problem, for which we propose modifications with respect to existing approaches. The results obtained even under highly sparse geometries are very promising, although further research is clearly needed in this direction.