IntraTomo: Self-supervised Learning-based Tomography via Sinogram Synthesis and Prediction

Guangming Zang, Ramzi Idoughi, Rui Li, Peter Wonka, Wolfgang Heidrich

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We propose IntraTomo, a powerful framework that combines the benefits of learning-based and model-based approaches for solving highly ill-posed inverse problems, in the Computed Tomography (CT) context. IntraTomo is composed of two core modules: a novel sinogram prediction module and a geometry refinement module, which are applied iteratively. In the first module, the unknown density field is represented as a continuous and differentiable function, parameterized by a deep neural network. This network is learned, in a self-supervised fashion, from the incomplete or/and degraded input sinogram. After getting estimated through the sinogram prediction module, the density field is consistently refined in the second module using local and non-local geometrical priors. With these two core modules, we show that IntraTomo significantly outperforms existing approaches on several ill-posed inverse problems, such as limited angle tomography with a range of 45 degrees, sparse view tomographic reconstruction with as few as eight views, or super-resolution tomography with eight times increased resolution. The experiments on simulated and real data show that our approach can achieve results of unprecedented quality.
Original languageEnglish (US)
Title of host publicationICCV
StatePublished - Sep 10 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-09-03
Acknowledgements: This work was supported by KAUST as part of the VCC Competitive Funding. The authors would like to thank the anonymous reviewers for their insightful comments.


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