提出新方法提升径向CT图像质量，解决截断伪影与细节丢失问题。
ProTCT: Projection quantification and fidelity constraint integrated deep reconstruction for Tangential CT
- 分析投影采样条件，给出实际系统设计指导
- 融合跨域保真约束网络，有效去除伪影并保留边缘细节
- 适用于大直径物体成像，适合工程检测场景
径向计算机断层扫描（TCT）是成像大直径样品（如石油管道和火箭）的有效工具。然而，探测器方向上的投影截断导致重建切片出现径向伪影。现有方法因投影域采样条件不明确及截面域过度平滑，难以获得高质量图像。本文提出一种集成投影量化与保真约束的深度重建方法（ProTCT），通过分析重建的采样条件，为TCT系统设计提供实用指南；同时引入一个跨投影域与截面域的深度去伪影网络及保真约束模块，显著提升结构还原能力与细节保持性。在模拟与真实数据集上验证，ProTCT在结构恢复与细节保留方面表现优异。本工作推动了大视场CT成像中采样条件探索与图像质量提升。
原文摘要 · Abstract (English)
Tangential computed tomography (TCT) is a useful tool for imaging the large-diameter samples, such as oil pipelines and rockets. However, TCT projections are truncated along the detector direction, resulting in degraded slices with radial artifacts. Meanwhile, existing methods fail to reconstruct decent images because of the ill-defined sampling condition in the projection domain and oversmoothing in the cross-section domain. In this paper, we propose a projection quantification and fidelity constraint integrated deep TCT reconstruction method (ProTCT) to improve the slice quality. Specifically, the sampling conditions for reconstruction are analysed, offering practical guidelines for TCT system design. Besides, a deep artifact-suppression network together with a fidelity-constraint module that operates across both projection and cross-section domains to remove artifacts and restore edge details. Demonstrated on simulated and real datasets, the ProTCT shows good performance in structure restoration and detail retention. This work contributes to exploring the sampling condition and improving the slice quality of TCT, further promoting the application of large view field CT imaging.
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