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.
X-ray luminescence imaging emerged for about a decade and combines both the high spatial resolution of x-ray imaging with the high measurement sensitivity of optical imaging, which could result in a great molecular imaging tool for small animals. So far, there are two types of x-ray luminescence computed tomography (XLCT) imaging. One uses a pencil beam x-ray for high spatial resolution at a cost of longer measurement time. The other uses cone beam x-ray to cover the whole mouse to obtain XLCT images at a very short time but with a compromised spatial resolution. Here we review these two methods in this paper and highlight the synthesized nanophosphors by different research groups.
We are building a focused x-ray luminescence tomography (FXLT) imaging system, developing a machinelearning based FXLT reconstruction algorithm, and synthesizing nanophosphors with different emission wavelengths. In this paper, we will report our current progress from these three aspects. Briefly, we mount all main components, including the focused x-ray tube, the fiber detector, and the x-ray tube and x-ray detector for a microCT system, on a rotary which is a heavy-duty ring track. A microCT scan will be performed before FXLT scan. For a FXLT scan, we will have four PMTs to measure four fiber detectors at two different wavelengths simultaneously for each linear scan position. We expect the spatial resolution of the FXLT imaging will be around 100 micrometers and a limit of detection of approximately 2 μg/mL (for Gd2O2S:Eu).
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