Sparse-view Computed Tomography (SVCT) reconstruction is an ill-posed inverse problem in imaging that aims to acquire high-quality CT images based on sparsely-sampled measurements. Recent works use Implicit Neural Representations (INRs) to build the coordinate-based mapping between sinograms and CT images. However, these methods have not considered the correlation between adjacent projection views, resulting in aliasing artifacts on SV sinograms. To address this issue, we propose a self-supervised SVCT reconstruction method -- Anti-Aliasing Projection Representation Field (APRF), which can build the continuous representation between adjacent projection views via the spatial constraints. Specifically, APRF only needs SV sinograms for training, which first employs a line-segment sampling module to estimate the distribution of projection views in a local region, and then synthesizes the corresponding sinogram values using center-based line integral module. After training APRF on a single SV sinogram itself, it can synthesize the corresponding dense-view (DV) sinogram with consistent continuity. High-quality CT images can be obtained by applying re-projection techniques on the predicted DV sinograms. Extensive experiments on CT images demonstrate that APRF outperforms state-of-the-art methods, yielding more accurate details and fewer artifacts. Our code will be publicly available soon.

翻译：稀疏视角计算机断层扫描（SVCT）重建是成像领域中的一个病态逆问题，旨在基于稀疏采样测量值获取高质量CT图像。近期研究利用隐式神经表示（INRs）构建正弦图与CT图像之间的基于坐标的映射。然而，这些方法未考虑相邻投影视角之间的相关性，导致SV正弦图上出现混叠伪影。为解决该问题，我们提出一种自监督SVCT重建方法——抗混叠投影表示场（APRF），该方法可通过空间约束建立相邻投影视角间的连续表示。具体而言，APRF仅需SV正弦图进行训练，首先利用线段采样模块估计局部区域内投影视角的分布，然后通过基于中心的线积分模块合成对应的正弦图值。在单一SV正弦图上训练APRF后，其可合成具有一致连续性的相应密集视角（DV）正弦图。通过将重投影技术应用于预测的DV正弦图，可获得高质量CT图像。在CT图像上的大量实验表明，APRF性能优于现有最优方法，能够生成更精确的细节并减少伪影。我们的代码将很快公开。