CTrex: A Research-Oriented Framework for Kernel- and Projection-Level Algorithm Development in CT Reconstruction
Abstract
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.
Related
- A complete forward projection algorithm through cylindrical and spherical refractive windows
- A Quantum Algorithm for the Radical of a Lie Algebra: Kernel Projection and Conditioning
- A Discretely Entropy Conserving/Stable Flux Reconstruction Scheme via Hybridized Split Form Differential Operator and Generalized Entropy Projection
- P^2-Gaussian: Prior-Driven and Projection-Guided Gaussian Splatting for Sparse-View Cone-Beam CT Reconstruction
- Comparison of spectral performance of three dual-energy CT scanners equipped with a deep-learning image reconstruction algorithm and one photon counting CT scanner: A phantom study
BibTeX
@misc{desplenter2026ctrex,
title = {{CTrex: A Research-Oriented Framework for Kernel- and Projection-Level Algorithm Development in CT Reconstruction}},
author = {Karel Desplenter and Wannes Goethals and Thomas De Schryver and Marjolein Heyndrickx and Jan Aelterman and Matthieu N. Boone},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.30166},
eprint = {2609.30166},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.30166},
}
RIS
TY - UNPB
TI - CTrex: A Research-Oriented Framework for Kernel- and Projection-Level Algorithm Development in CT Reconstruction
AU - Karel Desplenter
AU - Wannes Goethals
AU - Thomas De Schryver
AU - Marjolein Heyndrickx
AU - Jan Aelterman
AU - Matthieu N. Boone
PY - 2026
DA - 2026/09/24
PB - arXiv
DO - 10.48550/arxiv.2609.30166
UR - https://arxiv.org/abs/2609.30166
AB - 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.
ER -