用物理衰减先验提升CT重建，让3D图像更清晰。
$ρ$-NeRF: Leveraging Attenuation Priors in Neural Radiance Field for 3D Computed Tomography Reconstruction
- 用神经场建模3D体积，结合衰减参数ρ实现自监督学习。
- 相比FDK和CGLS，重建图像在投影合成与识别上精度显著提高。
- 适合做医学影像重建或需要物理约束的三维成像研究者。
本文提出ρ-NeRF，一种自监督方法，通过引入基于物理的衰减先验，显著提升新视角合成（NVS）与计算机断层扫描（CT）重建性能。ρ-NeRF利用全连接神经网络表示三维体数据，输入为连续的四维坐标（空间位置x, y, z）与初始衰减值ρ，输出对应位置的衰减系数。沿射线路径查询这些4D坐标，并应用经典前向投影技术对3D空间中的衰减数据进行积分。通过匹配并优化由传统重建算法（如Feldkamp-Davis-Kress算法，FDK或共轭梯度最小二乘法，CGLS）预初始化的衰减值，该增强模型在投影生成与图像识别任务中均表现出更高保真度。
原文摘要 · Abstract (English)
This paper introduces $ρ$-NeRF, a self-supervised approach that sets a new standard in novel view synthesis (NVS) and computed tomography (CT) reconstruction by modeling a continuous volumetric radiance field enriched with physics-based attenuation priors. The $ρ$-NeRF represents a three-dimensional (3D) volume through a fully-connected neural network that takes a single continuous four-dimensional (4D) coordinate, spatial location $(x, y, z)$ and an initialized attenuation value ($ρ$), and outputs the attenuation coefficient at that position. By querying these 4D coordinates along X-ray paths, the classic forward projection technique is applied to integrate attenuation data across the 3D space. By matching and refining pre-initialized attenuation values derived from traditional reconstruction algorithms like Feldkamp-Davis-Kress algorithm (FDK) or conjugate gradient least squares (CGLS), the enriched schema delivers superior fidelity in both projection synthesis and image recognition.
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