Accelerated X-Ray Fluorescence Computed Tomography via Multi-Pencil-Beam Excitation
Abstract
X-ray fluorescence computed tomography (XFCT), a form of X-ray molecular imaging, offers detailed quantitative imaging capabilities for high-Z metal nanoparticles (MNPs), which are widely studied for their applications in multifunctional theranostics. Due to its affordability and accessibility, the benchtop XFCT prototype typically employs a single-pixel detector (SPD) with single-pencil-beam (SPB) X-ray excitation. While this design (resembling the first-generation CT geometry) achieves reliable detection sensitivity, it is hindered by long imaging times. The use of simultaneous multiple-pencil-beam (MPB) excitation presents a promising solution to significantly reduce imaging times. In this study, we developed a repeatable workflow that combines Monte Carlo (MC) simulations and 3D printing to design Nbeam-MPB collimator, where Nbeam is the number of beams generated by the collimator. As an initial test, we fabricated a 2-MPB collimator and evaluated the performance of 2-MPB-based XFCT imaging on a physical phantom and small animals surgically implanted with agarose pellets containing gold chloride (H[AuCl4]). The results demonstrated a 2x acceleration in image acquisition without compromising the contrast-to-noise ratio (CNR). We further investigated the concept of Nbeam-MPB acceleration on the MC computational XFCT system, which confirmed the feasibility of achieving at least 4x acceleration with 4-MPB excitation. Combined with additional system optimization, such as X-ray beam flux optimization, XFCT imaging could be further accelerated, reducing acquisition time from hours to minutes and meeting the requirements for routine MNP imaging.
Keywords
Cite
@article{arxiv.2502.14524,
title = {Accelerated X-Ray Fluorescence Computed Tomography via Multi-Pencil-Beam Excitation},
author = {Ryder M. Schmidt and Daiki Hara and Jorge D. Vega and Marwan Abuhaija and Brett Bocian and Wendi Ma and Nesrin Dogan and Alan Pollack and Ge Wang and John C. Ford and Junwei Shi},
journal= {arXiv preprint arXiv:2502.14524},
year = {2025}
}