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Updated: Nov 12, 2025
High Spatial Resolution Chemical Imaging of Implant-Associated Infections with X-ray Excited Luminescence Chemical Imaging Through Tissue
Published on: September 30, 2022
Focused x-ray luminescence imaging system for small animals based on a rotary gantry
Michael C Lun1, Wenxiang Cong2, Mohammad Arifuzzaman3
1University of California, Merced, Department of Bioengineering, Merced, California, United States.
Significance:
The ability to detect and localize specific molecules through tissue is important for elucidating the molecular basis of disease and treatment. Unfortunately, most current molecular imaging tools in tissue either lack high spatial resolution (e.g., diffuse optical fluorescence tomography or positron emission tomography) or lack molecular sensitivity (e.g., micro-computed tomography, μCT). X-ray luminescence imaging emerged about 10 years ago to address this issue by combining the molecular sensitivity of optical probes with the high spatial resolution of x-ray imaging through tissue. In particular, x-ray luminescence computed tomography (XLCT) has been demonstrated as a powerful technique for the high-resolution imaging of deeply embedded contrast agents in three dimensions (3D) for small-animal imaging.
Aim:
To facilitate the translation of XLCT for small-animal imaging, we have designed and built a small-animal dedicated focused x-ray luminescence tomography (FXLT) scanner with a μCT scanner, synthesized bright and biocompatible nanophosphors as contrast agents, and have developed a deep-learning-based reconstruction algorithm.
Approach:
The proposed FXLT imaging system was designed using computer-aided design software and built according to specifications. NaGdF4 nanophosphors doped with europium or terbium were synthesized with a silica shell for increased biocompatibility and functionalized with biotin. A deep-learning-based XLCT image reconstruction was also developed based on the residual neural network as a data synthesis method of projection views from few-view data to enhance the reconstructed image quality.
Results:
We have built the FXLT scanner for small-animal imaging based on a rotational gantry. With all major imaging components mounted, the motor controlling the gantry can be used to rotate the system with a high accuracy. The synthesized nanophosphors displayed distinct x-ray luminescence emission, which enables multi-color imaging, and has successfully been bound to streptavidin-coated substrates. Lastly, numerical simulations using the proposed deep-learning-based reconstruction algorithm has demonstrated a clear enhancement in the reconstructed image quality.
Conclusions:
The designed FXLT scanner, synthesized nanophosphors, and deep-learning-based reconstruction algorithm show great potential for the high-resolution molecular imaging of small animals.
Insights
This study introduces a focused x-ray luminescence tomography (FXLT) system for high-resolution molecular imaging in small animals. The developed FXLT scanner, novel nanophosphors, and AI-driven reconstruction enhance imaging capabilities for disease research.
Area of Science:
- Biomedical Imaging
- Nanotechnology
- Medical Physics
Background:
- Current molecular imaging techniques in tissue lack either high spatial resolution or molecular sensitivity.
- X-ray luminescence imaging (XLI) combines optical probe sensitivity with X-ray resolution for improved tissue imaging.
- X-ray luminescence computed tomography (XLCT) offers high-resolution 3D imaging of contrast agents in small animals.
Purpose of the Study:
- To develop a dedicated focused x-ray luminescence tomography (FXLT) scanner for small-animal imaging.
- To synthesize novel, bright, and biocompatible nanophosphors for use as contrast agents.
- To create a deep-learning-based reconstruction algorithm to improve XLCT image quality.
Main Methods:
- Designed and constructed a small-animal dedicated FXLT scanner with integrated micro-computed tomography (μCT).
- Synthesized NaGdF4 nanophosphors doped with europium or terbium, coated with silica, and functionalized with biotin.
- Developed a deep-learning reconstruction algorithm using residual neural networks for enhanced image quality from few-view data.
Main Results:
- The FXLT scanner was successfully built with a high-accuracy rotational gantry.
- Synthesized nanophosphors exhibited multi-color emission and were successfully conjugated to streptavidin.
- Numerical simulations confirmed significant enhancement in reconstructed image quality using the deep-learning algorithm.
Conclusions:
- The developed FXLT scanner, nanophosphor contrast agents, and deep-learning reconstruction algorithm show promise for advanced small-animal molecular imaging.
- This integrated system has the potential to significantly advance research in disease mechanisms and treatment efficacy.
- The high-resolution molecular imaging capabilities can facilitate the translation of XLCT for preclinical applications.
