CoCPF: 用于成像中不适定逆问题的基于坐标的连续投影场
CoCPF: Coordinate-based Continuous Projection Field for Ill-Posed Inverse Problem in Imaging
AI总结：
针对稀疏视图CT重建中隐式神经表示方法存在孔洞的不适定问题，提出CoCPF方法，通过条带体采样与可微渲染填补孔洞、优化场域，在多类数据集与设置下实现更优的2D/3D重建效果。
摘要：
稀疏视图计算机断层扫描（SVCT）重建旨在基于稀疏采样的测量值获取CT图像。它能让受检者接受更少的电离辐射，降低终生患癌风险。近期研究采用隐式神经表示（INR）技术从单个SV正弦图重建CT图像，但由于不适定性，这些基于INR的方法可能在其场域中留下大量“孔洞”（即未建模空间），导致结果欠佳。本文提出基于坐标的连续投影场（Coordinate-based Continuous Projection Field, CoCPF），旨在为SVCT重建构建无孔洞的表示场，以实现更优的重建质量。具体而言，为填补孔洞，CoCPF首先采用基于条带的体采样模块，将Radon变换的采样区域从射线（一维空间）拓宽为条带（二维空间），可很好地覆盖SV投影之间的内部区域。随后，将采样区域输入所提出的可微渲染模块，孔洞可在训练过程中被联合优化，从而降低不适定程度。由此，CoCPF能够准确估计SV投影之间的内部测量值（即DV正弦图），经重投影后生成高质量CT图像。在模拟和真实投影数据集上的大量实验表明，CoCPF在不同投影数量和几何结构下的二维与三维SVCT重建任务中均优于现有最优方法，能生成更精细的细节且伪影更少。我们的代码将公开发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
