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arXiv · 2402.16921

Sparse2Inverse: Self-supervised inversion of sparse-view CT data

Nadja Gruber·Johannes Schwab·Elke Gizewski·Markus Haltmeier
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Abstract

Sparse-view computed tomography (CT) enables fast and low-dose CT imaging, an essential feature for patient-save medical imaging and rapid non-destructive testing. In sparse-view CT, only a few projection views are acquired, causing standard reconstructions to suffer from severe artifacts and noise. To address these issues, we propose a self-supervised image reconstruction strategy. Specifically, in contrast to the established Noise2Inverse, our proposed training strategy uses a loss function in the projection domain, thereby bypassing the otherwise prescribed nullspace component. We demonstrate the effectiveness of the proposed method in reducing stripe-artifacts and noise, even from highly sparse data.

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Nadja Gruber, Johannes Schwab, Elke Gizewski, Markus Haltmeier. 2024-02-26. Sparse2Inverse: Self-supervised inversion of sparse-view CT data. https://arxiv.org/abs/2402.16921

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