A diffusion-based generative prior approach to sparse-view computed tomography.

Authors

Davide Evangelista, Pasquale Cascarano, Elena Loli Piccolomini

Published in

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society. Volume 134. Pages 102802. Aug 04, 2026. Epub Aug 04, 2026.

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.

PMID:
42556027
Bibliographic data and abstract were imported from PubMed on 06 Aug 2026.

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