用AI增强断层扫描，解决角度受限导致的图像失真问题。
Limited-angle x-ray nano-tomography with machine-learning enabled iterative reconstruction engine
- 将CNN与感知知识结合，作为迭代重建的智能正则化器。
- 在超过100度缺失楔形情况下仍显著提升重建质量。
- 对稀疏投影也有效，适合真实世界复杂成像场景。
断层扫描长期面临'缺失楔形'难题，即因几何限制无法获取特定角度范围内的投影图像，导致重建图像出现严重伪影且分辨率低下。为应对这一挑战，本文提出感知融合迭代断层重建引擎（Perception Fused Iterative Tomography Reconstruction Engine），将卷积神经网络（CNN）与感知知识作为智能正则化项融入迭代求解框架。采用交替方向乘子法（ADMM）在物理域与图像域同步优化，实现物理一致性与视觉质量双重提升。通过多种基于不同X射线显微技术的实验数据集验证，即使在超过100度缺失楔形条件下，重建效果仍显著优于传统方法；此外，该方法在稀疏投影场景下亦表现优异，尽管网络未专门针对此训练。这表明其在解决实际3D X射线成像中常见挑战方面具备鲁棒性与通用性。
原文摘要 · Abstract (English)
A long-standing challenge in tomography is the 'missing wedge' problem, which arises when the acquisition of projection images within a certain angular range is restricted due to geometrical constraints. This incomplete dataset results in significant artifacts and poor resolution in the reconstructed image. To tackle this challenge, we propose an approach dubbed Perception Fused Iterative Tomography Reconstruction Engine, which integrates a convolutional neural network (CNN) with perceptional knowledge as a smart regularizer into an iterative solving engine. We employ the Alternating Direction Method of Multipliers to optimize the solution in both physics and image domains, thereby achieving a physically coherent and visually enhanced result. We demonstrate the effectiveness of the proposed approach using various experimental datasets obtained with different x-ray microscopy techniques. All show significantly improved reconstruction even with a missing wedge of over 100 degrees - a scenario where conventional methods fail. Notably, it also improves the reconstruction in case of sparse projections, despite the network not being specifically trained for that. This demonstrates the robustness and generality of our method of addressing commonly occurring challenges in 3D x-ray imaging applications for real-world problems.
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