用学习型原始对偶法实现单视角断层成像重建，突破传统限制。
Single-View Tomographic Reconstruction Using Learned Primal Dual
- 基于学习的原始对偶框架，融合物理模型与数据驱动方法。
- 在单视角下实现轴对称目标的高质量重建，优于传统数值反演方法。
- 适用于低发散平行光和锥形束成像场景，适合医学或工业检测应用。
学习型原始对偶（LPD）方法在多种断层成像模态中表现出色，尤其在视图角度受限或视图数量少等挑战性条件下。本文研究其在更极端情形——轴对称目标的单视角断层成像中的表现。考虑两种模态：第一种假设为低发散或平行射线；第二种模拟锥形束X射线成像实验装置。两种情况下，训练数据均通过闭合形式积分变换或基于物理的射线追踪软件生成，并加入模糊与噪声。重建结果与常见的数值反演方法进行对比。
原文摘要 · Abstract (English)
The Learned Primal Dual (LPD) method has shown promising results in various tomographic reconstruction modalities, particularly under challenging acquisition restrictions such as limited viewing angles or a limited number of views. We investigate the performance of LPD in a more extreme case: single-view tomographic reconstructions of axially-symmetric targets. This study considers two modalities: the first assumes low-divergence or parallel X-rays. The second models a cone-beam X-ray imaging testbed. For both modalities, training data is generated using closed-form integral transforms, or physics-based ray-tracing software, then corrupted with blur and noise. Our results are then compared against common numerical inversion methodologies.
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