Abstract
Sparse-view Computed Tomography (CT) reconstructs images from a limited number of X-ray projections to reduce radiation and scanning time, which makes reconstruction an ill-posed inverse problem. Deep learning methods achieve high-fidelity reconstructions but often overfit to a fixed acquisition setup, failing to generalize across sampling rates and image resolutions. For example, convolutional neural networks (CNNs) use the same learned kernels across resolutions, leading to artifacts when data resolution changes. We propose Computed Tomography neural Operator (CTO), a unified CT reconstruction framework that extends to continuous function space, enabling generalization (without retraining) across sampling rates and image resolutions. CTO operates jointly in the sinogram and image domains through rotation-equivariant Discrete-Continuous convolutions parametrized in the function space, making it inherently resolution- and sampling-agnostic. Empirically, CTO enables consistent multi-sampling-rate and cross-resolution performance, with on average >4dB PSNR gain over CNNs. Compared to state-of-the-art diffusion methods, CTO is 500$\times$ faster in inference time with on average 3dB gain. Empirical results also validate our design choices behind CTO's sinogram-space operator learning and rotation-equivariant convolution. Overall, CTO outperforms state-of-the-art baselines across sampling rates and resolutions, offering a scalable and generalizable solution that makes automated CT reconstruction more practical for deployment.
Abstract (translated)
稀疏视图计算断层成像（CT）是从有限数量的X射线投影中重建图像的技术，这种方法可以减少辐射剂量和扫描时间。然而，这种技术使得重建过程成为了一个病态反问题。深度学习方法虽然能实现高保真度的重建，但常常会在固定的采集设置下过拟合，无法跨采样率和图像分辨率进行泛化。例如，卷积神经网络（CNN）使用相同的学习核函数在不同分辨率间应用时会导致伪影产生。 为此，我们提出了一种新的计算断层成像神经操作器(CTO)，这是一种统一的CT重建框架，它可以扩展到连续功能空间中，从而能够跨越采样率和图像分辨率进行泛化（无需重新训练）。CTO同时在投影图（sinogram）域和图像域中运行，并通过旋转等变离散-连续卷积在函数空间参数化，使其本质上对于分辨率变化和不同采样方式都具备鲁棒性。 实验结果显示，CTO能够在多级采样率和跨分辨率上保持一致的性能，并且平均而言比CNNs提高了超过4dB的峰值信噪比（PSNR）。与最先进的扩散方法相比，CTO在推断时间上快了500倍，而平均PSNR提升了3dB。实验结果也验证了我们在CTO的设计中关于投影图空间操作学习和旋转等变卷积的选择。 总的来说，无论采样率还是分辨率如何变化，CTO都能超越现有的基线方法，在自动化CT重建领域提供了可扩展且泛化的解决方案，使得其在实际部署中的应用更加实用。
URL
https://arxiv.org/abs/2512.12236
