CTrex：用于CT重建中核级与投影级算法开发的研究导向框架

中文摘要 AI 辅助

微型计算机断层扫描（micro-CT）越来越多地应用于物体运动、变形、截断视野、连续旋转或非常规扫描协议违反经典重建管线假设的成像场景。现有重建框架能高效支持标准几何和工作流程，但对投影算子如何离散化和评估的控制有限。当精度依赖于射线采样、插值或算子耦合时，这限制了方法论探索。我们引入CTrex，一个研究导向、GPU加速的迭代重建框架，将投影-校正-反投影管线暴露到核级。CTrex不将这些算子视为固定的黑盒，而是将其表示为统一迭代结构内可编辑的计算构建块。这允许非理想采集效应直接纳入数值算子，而非作为外部校正处理。CTrex结合GPU级可访问性与基于齐次坐标视图矩阵的可扩展几何公式，使刚性和仿射变换、探测器错位及时变几何得以一致表达。它支持圆形、螺旋、偏移和传送带轨迹，同时保持GPU核与轨迹定义无关。在运动和变形感知重建、基于事件的4D成像、柱坐标重建及扩展视野CT中的应用，展示了算子级适应如何实现传统框架难以实现的策略。CTrex为现实和非常规成像条件下的CT重建研究提供了灵活平台。

英文摘要

Micro-computed tomography (micro-CT) is increasingly applied to imaging scenarios in which object motion, deformation, truncated fields of view, continuous rotation, or unconventional scanning protocols violate assumptions underlying classical reconstruction pipelines. Existing reconstruction frameworks efficiently support standard geometries and workflows but offer limited control over how projection operators are discretized and evaluated. This restricts methodological exploration when accuracy depends on ray sampling, interpolation, or operator coupling. We introduce CTrex, a research-oriented, GPU-accelerated iterative reconstruction framework that exposes the projection-correction-backprojection pipeline down to the kernel level. Rather than treating these operators as fixed black boxes, CTrex represents them as editable computational building blocks within a unified iterative structure. This allows non-ideal acquisition effects to be incorporated directly into the numerical operators rather than treated as external corrections. CTrex combines GPU-level accessibility with an extensible geometry formulation based on homogeneous-coordinate view matrices, allowing rigid and affine transformations, detector misalignments, and time-varying geometries to be expressed consistently. It supports circular, helical, offset, and conveyor-belt trajectories while keeping GPU kernels agnostic to the trajectory definition. Applications in motion- and deformation-aware reconstruction, event-based 4D imaging, cylindrical-coordinate reconstruction, and extended-field-of-view CT illustrate how operator-level adaptations enable strategies difficult to realize in conventional frameworks. CTrex provides a flexible platform for CT reconstruction research under realistic and unconventional imaging conditions.

发表机构

• Ghent university（根特大学）

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