Sparse-view computed tomography (SVCT) reconstruction aims to acquire CT images based on sparsely-sampled measurements. It allows the subjects exposed to less ionizing radiation, reducing the lifetime risk of developing cancers. Recent researches employ implicit neural representation (INR) techniques to reconstruct CT images from a single SV sinogram. However, due to ill-posedness, these INR-based methods may leave considerable ``holes'' (i.e., unmodeled spaces) in their fields, leading to sub-optimal results. In this paper, we propose the Coordinate-based Continuous Projection Field (CoCPF), which aims to build hole-free representation fields for SVCT reconstruction, achieving better reconstruction quality. Specifically, to fill the holes, CoCPF first employs the stripe-based volume sampling module to broaden the sampling regions of Radon transformation from rays (1D space) to stripes (2D space), which can well cover the internal regions between SV projections. Then, by feeding the sampling regions into the proposed differentiable rendering modules, the holes can be jointly optimized during training, reducing the ill-posed levels. As a result, CoCPF can accurately estimate the internal measurements between SV projections (i.e., DV sinograms), producing high-quality CT images after re-projection. Extensive experiments on simulated and real projection datasets demonstrate that CoCPF outperforms state-of-the-art methods for 2D and 3D SVCT reconstructions under various projection numbers and geometries, yielding fine-grained details and fewer artifacts. Our code will be publicly available.

翻译：稀疏视图计算机断层扫描（SVCT）重建旨在基于稀疏采样测量获取CT图像。该方法可使受检者暴露于更少的电离辐射，从而降低罹患癌症的终身风险。近期研究采用隐式神经表示（INR）技术从单幅SV正弦图重建CT图像。然而，由于问题的不适定性，这些基于INR的方法可能在其表示场中留下显著的“空洞”（即未建模空间），导致次优结果。本文提出基于坐标的连续投影场（CoCPF），旨在为SVCT重建构建无空洞的表示场，以获得更优的重建质量。具体而言，为填补空洞，CoCPF首先采用基于条带的体采样模块，将拉东变换的采样区域从射线（一维空间）扩展至条带（二维空间），从而充分覆盖SV投影间的内部区域。随后，通过将采样区域输入至所提出的可微分渲染模块，空洞可在训练过程中被联合优化，从而降低问题的不适定程度。最终，CoCPF能够准确估计SV投影间的内部测量值（即DV正弦图），并通过重投影生成高质量CT图像。在模拟和真实投影数据集上的大量实验表明，在不同投影数量和几何配置下，CoCPF在二维和三维SVCT重建任务中均优于现有先进方法，能生成更精细的细节和更少的伪影。我们的代码将公开提供。