Resolution-Agnostic Neural Operators for Multi-Rate Sparse-View CT

Sparse-view Computed Tomography (CT) reconstructs im-ages from a limited number of X-ray projections to reduce radiation andscanning time, which is an ill-posed inverse problem. Existing methodsachieve high-fidelity reconstructions but overfit to a fixed acquisitionsetup, failing to generalize well across sampling rates. For example, con-volutional neural networks (CNNs) use the same kernels across resolu-tions, leading to artifacts when data resolution changes. This is a criticallimitation in clinical practice, where acquisition sampling settings varyacross organs and diagnostic protocols.We propose Computed Tomography neural Operator (CTO), the firstneural operator (NO) framework for CT reconstruction. CTO extendslearning from fixed discretized grids to continuous function space, en-abling a single model to generalize across measurement sampling rateswithout retraining. We also propose new NO architectural designs forCT: (i) a dual-domain NO architecture in both sinogram and imagespaces, capturing complementary spatial–frequency information, and (ii)rotation-equivariant DIScrete–COntinuous convolutions (DISCO) thatexploit the rotational structure inherent in tomographic acquisition. Em-pirically, CTO outperform CNNs (> 3.4dB PSNR gain) and other base-lines in multi-resolution settings across multiple CT datasets. Comparedto state-of-the-art di!usion methods, CTO has 500→ faster inferencewith an average 3dB gain. CTO further demonstrates strong out-of-distribution robustness, maintaining gains under cross-dataset transferand noisy sinogram conditions. Ablation studies also validate each de-sign choice. CTO establishes neural operators as a principled and practi-cal paradigm for flexible, discretization-agnostic CT reconstruction. Ourcode is available at https://github.com/neuraloperator/sparse_ct.