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The authors developed CTrex, a GPU-accelerated iterative reconstruction framework that exposes the projection-correction-backprojection pipeline down to the computational kernel level. Existing CT reconstruction software packages treat projection operators as black boxes, severely limiting researcher control over ray discretization, interpolation, and the incorporation of dynamic acquisition physics. By decoupling trajectory definitions via homogeneous-coordinate view matrices and enabling kernel-level operator customization, CTrex achieves high-fidelity reconstruction across challenging regimes including deformation-aware 4D imaging, event-based sensing, and extended fields of view.
Breaking open CT reconstruction's black-box projection operators at the GPU kernel level allows severe real-world artifacts—like continuous motion and non-rigid deformation—to be integrated directly into the numerical forward model rather than patched post hoc.
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.