DiffNR：面向稀疏视图三维断层成像重建的扩散增强神经表征优化

摘要

神经表示（NRs），如神经场和3D高斯函数，能有效建模计算机断层扫描（CT）中的体数据，但在稀疏视角条件下会出现严重伪影。为此，我们提出DiffNR这一新颖框架，通过扩散先验增强神经表示的优化能力。其核心是SliceFixer——一个专用于修复退化切片中伪影的单步扩散模型。我们在网络中集成专用条件层，并开发定制化数据管理策略以支持模型微调。在重建过程中，SliceFixer定期生成伪参考体数据，通过辅助的3D感知监督来修正欠约束区域。相较于先前将CT求解器嵌入耗时迭代去噪过程的方法，我们提出的"修复-增强"策略避免了频繁查询扩散模型，从而获得更优的运行时效。大量实验表明，DiffNR平均将PSNR提升3.99 dB，具有良好的跨领域泛化能力，并能保持高效的优化过程。

English

Neural representations (NRs), such as neural fields and 3D Gaussians, effectively model volumetric data in computed tomography (CT) but suffer from severe artifacts under sparse-view settings. To address this, we propose DiffNR, a novel framework that enhances NR optimization with diffusion priors. At its core is SliceFixer, a single-step diffusion model designed to correct artifacts in degraded slices. We integrate specialized conditioning layers into the network and develop tailored data curation strategies to support model finetuning. During reconstruction, SliceFixer periodically generates pseudo-reference volumes, providing auxiliary 3D perceptual supervision to fix underconstrained regions. Compared to prior methods that embed CT solvers into time-consuming iterative denoising, our repair-and-augment strategy avoids frequent diffusion model queries, leading to better runtime performance. Extensive experiments show that DiffNR improves PSNR by 3.99 dB on average, generalizes well across domains, and maintains efficient optimization.

DiffNR：面向稀疏视图三维断层成像重建的扩散增强神经表征优化

DiffNR: Diffusion-Enhanced Neural Representation Optimization for Sparse-View 3D Tomographic Reconstruction

摘要

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