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

2024/02/26 by Gruber, Nadja, Schwab, Johannes, Gizewski, Elke +1 · 2 citations
#FOS: Electrical engineering #Image and Video Processing (eess.IV) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2402.16921

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.

Cited by

• Locally-Supervised Global Image Restoration
• Equivariance2Inverse: A Practical Self-Supervised CT Reconstruction Method Benchmarked on Real, Limited-Angle, and Blurred Data

Related

• Learning image from projection: a full-automatic reconstruction (FAR) net for sparse-views computed tomography
• A Lightweight Structure Aimed to Utilize Spatial Correlation for Sparse-View CT Reconstruction
• Learning to Distill Global Representation for Sparse-View CT
• Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction
• CoCPF: Coordinate-based Continuous Projection Field for Ill-Posed Inverse Problem in Imaging