SONAR：一种结构一致的神经算子，用于零空间感知的稀疏视图CT重建
SONAR: A Structure-Consistent Neural Operator for Null-Space-Aware Sparse View CT Reconstruction
- University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）
- Peking University Health Science Center（北京大学医学部）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
SONAR提出一种结构一致神经算子，通过预测低维零空间感知表示并分离测量与伪测量残差，实现稀疏视图CT的准确、稳健且跨离散化重建。
AI中文摘要：
稀疏视图计算机断层扫描（CT）可减少辐射剂量和采集时间，但由于投影不完整，对零空间信息的约束不足，仍存在严重的病态性问题。现有的基于学习的方法通常在高维图像空间中估计该信息，将物理测量误差与预测误差混为一谈，并依赖于固定的离散化方式。我们提出SONAR，一种用于零空间感知重建的结构一致神经算子。SONAR并非恢复完整的零空间分量，而是从采集的投影中预测一个低维的零空间感知表示，作为伪测量。它将测量残差和伪测量残差分离，通过物理算子将其提升到图像域，并应用独立的神经算子来约束其结构效应，从而在抑制无支撑结构的同时容纳可接受的误差。为支持跨离散化重建，各向异性U形神经算子利用方向相关的连续支撑来建模周期性角度维度和非周期性探测器维度，而图像域神经算子在目标网格上重新离散化连续核。这些组件构成一个受优化启发的展开网络。在模拟AAPM和临床MARS光子计数CT数据上的实验表明，在已见和未见视图设置以及未见图像分辨率下均有一致的改进。在AAPM数据集上，与最强的竞争方法相比，SONAR在62个视图下将PSNR提高了1.87分贝，在零样本迁移到512×512网格时提高了7.63分贝。SONAR在所有评估的临床设置中也取得了最佳整体性能，展示了准确、结构可靠且对离散化稳健的稀疏视图CT重建。
英文摘要：
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.
