In the present work, we propose a Self-supervised COordinate Projection nEtwork (SCOPE) to reconstruct the artifacts-free CT image from a single SV sinogram by solving the inverse tomography imaging problem. Compared with recent related works that solve similar problems using implicit neural representation network (INR), our essential contribution is an effective and simple re-projection strategy that pushes the tomography image reconstruction quality over supervised deep learning CT reconstruction works. The proposed strategy is inspired by the simple relationship between linear algebra and inverse problems. To solve the under-determined linear equation system, we first introduce INR to constrain the solution space via image continuity prior and achieve a rough solution. And secondly, we propose to generate a dense view sinogram that improves the rank of the linear equation system and produces a more stable CT image solution space. Our experiment results demonstrate that the re-projection strategy significantly improves the image reconstruction quality (+3 dB for PSNR at least). Besides, we integrate the recent hash encoding into our SCOPE model, which greatly accelerates the model training. Finally, we evaluate SCOPE in parallel and fan X-ray beam SVCT reconstruction tasks. Experimental results indicate that the proposed SCOPE model outperforms two latest INR-based methods and two well-popular supervised DL methods quantitatively and qualitatively.

翻译：本文提出了一种自监督坐标投影网络（SCOPE），通过求解逆断层成像问题，从单张稀疏视角正弦图中重建无伪影的CT图像。与近期采用隐式神经表示网络（INR）解决类似问题的相关工作相比，我们的核心贡献在于提出了一种有效且简单的重投影策略，使得断层成像重建质量超越了有监督深度学习CT重建方法。该策略的灵感来源于线性代数与逆问题之间的简单关系。为求解欠定线性方程组，我们首先引入INR，利用图像连续性先验约束解空间，获得初步近似解；随后提出生成密集视角正弦图，以提升线性方程组的秩，从而构建更稳定的CT图像解空间。实验结果表明，重投影策略显著提升了图像重建质量（峰值信噪比至少提升3分贝）。此外，我们将近期提出的哈希编码集成到SCOPE模型中，大幅加速了模型训练。最后，我们在平行束和扇形束X射线稀疏视角CT重建任务中评估了SCOPE模型。实验结果表明，所提出的SCOPE模型在定量和定性上均优于两种最新的基于INR的方法和两种广受欢迎的有监督深度学习方法。