Direct Trajectory Reconstruction for Fast 3D X-ray Particle Tracking Velocimetry in Porous Media
Abstract
X-ray tomographic particle tracking velocimetry is a promising tool to provide pore-scale insight into fluid flow inside opaque porous media. This is critical for applications ranging from geo-energy to electrochemistry. Current approaches to implement this three-dimensional, three-component (3D-3C) velocimetry technique use conventional micro-computed tomography (CT) workflows to recover tracer particle trajectories. These workflows first reconstruct a sequence of 3D CT images from radiographs which are sequentially acquired at time intervals that span multiple viewing angles. The tracer particles are then detected in each of these CT images and finally linked into trajectories. However, motion-blurring induced by the static CT volume reconstruction impairs the detectability of fast-moving tracer particles, severely limiting the temporal resolution and dynamic range of the velocimetry outcome. In this work, we show that this limitation can be overcome by explicitly recovering particle trajectories directly from the acquired radiographs, bypassing the intermediate tomographic reconstruction step and the static-scene assumptions of traditional 3D CT reconstructions. We implement this approach in a new open-source algorithm, CTracks, and validate it using experimental data on pipe flow and on flow through a complex porous medium, demonstrating a five-fold increase in the maximum detectable velocity compared with state-of-the-art methods. Numerical simulations further confirm improved detection rates and bounded trajectory errors at these increased velocities. Our work thus demonstrates that 3D-3C velocimetry in opaque microscopic geometries can be extended to substantially faster flows by replacing X-ray tomographic image reconstruction with direct particle trajectory reconstruction.
Related
- CBTV: Colour Bean Tracking Velocimetry for Dense Granular Flows
- Dual Laser and Shadow Particle Image Velocimetry for Multiscale Fluid-Structure Interactions
- ART of PIV: Agentic Real-Time Particle Image Velocimetry for Active Flow Control
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BibTeX
@misc{merwe2026direct,
title = {{Direct Trajectory Reconstruction for Fast 3D X-ray Particle Tracking Velocimetry in Porous Media}},
author = {Robert van der Merwe and Wannes Goethals and Sharon Ellman and Sojwal Manoorkar and Jan Aelterman and Matthieu N. Boone and Tom Bultreys},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.16838},
eprint = {2609.16838},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.16838},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - Direct Trajectory Reconstruction for Fast 3D X-ray Particle Tracking Velocimetry in Porous Media
AU - Robert van der Merwe
AU - Wannes Goethals
AU - Sharon Ellman
AU - Sojwal Manoorkar
AU - Jan Aelterman
AU - Matthieu N. Boone
AU - Tom Bultreys
PY - 2026
DA - 2026/09/15
PB - arXiv
DO - 10.48550/arxiv.2609.16838
UR - https://arxiv.org/abs/2609.16838
AB - X-ray tomographic particle tracking velocimetry is a promising tool to provide pore-scale insight into fluid flow inside opaque porous media. This is critical for applications ranging from geo-energy to electrochemistry. Current approaches to implement this three-dimensional, three-component (3D-3C) velocimetry technique use conventional micro-computed tomography (CT) workflows to recover tracer particle trajectories. These workflows first reconstruct a sequence of 3D CT images from radiographs which are sequentially acquired at time intervals that span multiple viewing angles. The tracer particles are then detected in each of these CT images and finally linked into trajectories. However, motion-blurring induced by the static CT volume reconstruction impairs the detectability of fast-moving tracer particles, severely limiting the temporal resolution and dynamic range of the velocimetry outcome. In this work, we show that this limitation can be overcome by explicitly recovering particle trajectories directly from the acquired radiographs, bypassing the intermediate tomographic reconstruction step and the static-scene assumptions of traditional 3D CT reconstructions. We implement this approach in a new open-source algorithm, CTracks, and validate it using experimental data on pipe flow and on flow through a complex porous medium, demonstrating a five-fold increase in the maximum detectable velocity compared with state-of-the-art methods. Numerical simulations further confirm improved detection rates and bounded trajectory errors at these increased velocities. Our work thus demonstrates that 3D-3C velocimetry in opaque microscopic geometries can be extended to substantially faster flows by replacing X-ray tomographic image reconstruction with direct particle trajectory reconstruction.
ER -