Focused x-ray luminescence imaging system for small animals based on a rotary gantry

Abstract

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.

Keywords: deep learning; molecular imaging; nanoparticles; optical tomography; x-ray imaging.

Fig. 1

The FXLT Imaging System. (a) Proposed scanning scheme for the FXLT system; (b) CAD model of the proposed system; and (c) physical build of the scanner.

Fig. 2

(a) Luminescence spectra of ${\mathrm{NaGdF}}_4:\mathrm{Eu}$ (red) and ${\mathrm{NaGdF}}_4:\mathrm{Tb}$ (green) nanophosphors, TEM images above the spectra show silica coated particles. (b) TEM image of biotin-coated nanophosphors (top), and STEM image of biotin-coated nanophosphors adhered to streptavidin coated microspheres (bottom), inset image is a zoomed-in view.

Fig. 3

Projection data synthesis and image reconstruction. (a) Sinogram of 15 projection views; (b) sinogram of 30 projection views; (c) reconstructed sinogram from 15 projection views; (d) image reconstructed from 15 projection views; (e) ground truth image of phantom at a plane; and (f) image reconstructed from reconstructed 30 projection views.