Problem
Research questions and friction points this paper is trying to address.
Sparse-view CT
null-space information
ill-posed problem
learning-based methods
fixed discretizations
SONAR: A Structure-Consistent Neural Operator for Null-Space-Aware Sparse View CT Reconstruction
🤖 AI Summary
为解决稀疏视图CT重建问题，提出SONAR方法，通过预测低维空域感知表示并分离测量与伪测量残差，实现结构一致性和跨离散化重建。
📝 Abstract
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 residuals, lifts them into the image domain through physics operators, and applies independent neural operators to constrain their structural effects, thereby accommodating admissible errors while suppressing unsupported structures. To support cross-discretization reconstruction, an anisotropic U-shaped neural operator models the periodic angular and nonperiodic detector dimensions using direction-dependent continuous supports, while image-domain neural operators re-discretize continuous kernels on target grids. These components form an optimization-inspired unrolled network. Experiments on simulated AAPM and clinical MARS photon-counting CT data demonstrate consistent improvements across seen and unseen view settings and unseen image resolutions. On AAPM dataset, SONAR improves PSNR by 1.87~dB at 62 views and by 7.63~dB under zero-shot transfer to a $512\times512$ grid over the strongest competing methods. SONAR also achieves the best overall performance in all clinical settings evaluated, demonstrating accurate, structurally reliable, and discretization-robust sparse-view CT reconstruction.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Null-Space-Aware
Structure-Consistent Neural Operator
Sparse-View CT Reconstruction
Cross-Discretization
Pseudo-Measurements
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