用12个数据点实现低角度CT重建，效果媲美大量数据方法。
Data-Efficient Limited-Angle CT Using Deep Priors and Regularization
- 融合深度先验与多种正则化，解决角度受限下的图像重建难题。
- 仅用12个数据点（8个训练+4个调参）即达到顶尖合成数据方法水平。
- 适合辐射暴露敏感场景，如医学成像中的快速低剂量扫描。
从Radon变换中重构图像是计算机断层扫描（CT）中的核心任务，广泛应用于X射线扫描。在许多实际场景中，完整180度扫描不可行，或需降低辐射剂量。此时问题变得病态，传统全视角方法会产生显著伪影。本文提出一种极低数据量的有限角度CT重建方法。由于逆问题病态，我们结合了总变差、sinogram滤波、深度图像先验和块级自编码器等多种正则化手段，并采用可微分的Radon变换，使梯度优化成为可能。在赫尔辛基断层扫描挑战赛2022数据集上评估，目标是从有限角度sinogram重建二值圆盘。仅使用12个数据点（8个用于学习先验，4个用于超参数选择），重建结果媲美最佳合成数据驱动方法。
原文摘要 · Abstract (English)
Reconstructing an image from its Radon transform is a fundamental computed tomography (CT) task arising in applications such as X-ray scans. In many practical scenarios, a full 180-degree scan is not feasible, or there is a desire to reduce radiation exposure. In these limited-angle settings, the problem becomes ill-posed, and methods designed for full-view data often leave significant artifacts. We propose a very low-data approach to reconstruct the original image from its Radon transform under severe angle limitations. Because the inverse problem is ill-posed, we combine multiple regularization methods, including Total Variation, a sinogram filter, Deep Image Prior, and a patch-level autoencoder. We use a differentiable implementation of the Radon transform, which allows us to use gradient-based techniques to solve the inverse problem. Our method is evaluated on a dataset from the Helsinki Tomography Challenge 2022, where the goal is to reconstruct a binary disk from its limited-angle sinogram. We only use a total of 12 data points--eight for learning a prior and four for hyperparameter selection--and achieve results comparable to the best synthetic data-driven approaches.
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