SONAR: A Structure-Consistent Neural Operator for Null-Space-Aware Sparse View CT Reconstruction
Sparse-view computed tomography (CT) reduces radiation dose and acquisition time but remains severely ill-posed because incomplete projections poorly constrain null-space information. Existing learning-based methods often estimate this information in high-dimensional image space, conflate physical measurement errors with prediction errors, and depend on fixed discretizations. We propose SONAR, a Structure-Consistent Neural Operator for Null-Space-Aware Reconstruction. Instead of recovering the full null-space component, SONAR predicts a low-dimensional null-space-aware representation from the acquired projections as pseudo-measurements. It separates measurement and pseudo-measurement resi...
| Authors | Song Ni, Haijun Yu, Haodong Li et al. |
|---|---|
| Primary category | eess.IV |
| Published | 12 September 2026 |
| Last updated at source | 12 September 2026 |
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