Stored luminescence computed tomography

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

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 for 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 machine-learning 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).

Feasibility study of three-dimensional multiple-beam x-ray luminescence tomography

X-ray luminescence tomography (XLT) is a promising imaging technology based on x-ray beams, with high-resolution capability. We developed a fan-beam XLT system, where the x-ray beam scans the object at predefined directions and positions. As the scanning at one position needs to cover the object, the data acquisition time is usually long. To improve spatial resolution, we propose a three-dimensional multiple-beam x-ray luminescence imaging method, in which the x rays are modulated by an x-ray fence-modulation component. The proposed method can produce multiple x-ray beams and ensure spatial resolution along the longitudinal direction as well as the transverse plane. The proposed methods of single-source experiments can achieve 0.62 mm in location error and 0.87 in the dice coefficient while 1.32 mm in location error and 0.63 in the dice coefficient in the double-source experiment. The simulation experiments show that our proposed method can achieve better results at different depths than the traditional scanning method. It is also demonstrated that the best simulation results can be achieved with the smallest x-ray width.

Method for improving the spatial resolution of narrow x-ray beam-based x-ray luminescence computed tomography imaging

X-ray luminescence computed tomography (XLCT) is an emerging hybrid imaging modality which has the potential for achieving both high sensitivity and spatial resolution simultaneously. For the narrow x-ray beam-based XLCT imaging, based on previous work, a spatial resolution of about double the x-ray beam size can be achieved using a translate/rotate scanning scheme, taking step sizes equal to the x-ray beam width. To break the current spatial resolution limit, we propose a scanning strategy achieved by reducing the scanning step size to be smaller than the x-ray beam size. We performed four sets of numerical simulations and a phantom experiment using cylindrical phantoms and have demonstrated that our proposed scanning method can greatly improve the XLCT-reconstructed image quality compared with the traditional scanning approach. In our simulations, by using a fixed x-ray beam size of 0.8 mm, we were able to successfully reconstruct six embedded targets as small as 0.5 mm in diameter and with the same edge-to-edge distances by using a scanning step as small as 0.2 mm which is a 1.6 times improvement in the spatial resolution compared with the traditional approach. Lastly, the phantom experiment further demonstrated the efficacy of our proposed method in improving the XLCT image quality, with all image quality metrics improving as the step size decreased.

Chemically engineered persistent luminescence nanoprobes for bioimaging

Imaging nanoprobes are a group of nanosized agents developed for providing improved contrast for bioimaging. Among various imaging probes, optical sensors capable of following biological events or progresses at the cellular and molecular levels are actually actively developed for early detection, accurate diagnosis, and monitoring of the treatment of diseases. The optical activities of nanoprobes can be tuned on demand by chemists by engineering their composition, size and surface nature. This review will focus on researches devoted to the conception of nanoprobes with particular optical properties, called persistent luminescence, and their use as new powerful bioimaging agents in preclinical assays.

X-ray luminescence computed tomography imaging based on X-ray distribution model and adaptively split Bregman method

X-ray luminescence computed tomography (XLCT) has become a promising imaging technology for biological application based on phosphor nanoparticles. There are mainly three kinds of XLCT imaging systems: pencil beam XLCT, narrow beam XLCT and cone beam XLCT. Narrow beam XLCT can be regarded as a balance between the pencil beam mode and the cone-beam mode in terms of imaging efficiency and image quality. The collimated X-ray beams are assumed to be parallel ones in the traditional narrow beam XLCT. However, we observe that the cone beam X-rays are collimated into X-ray beams with fan-shaped broadening instead of parallel ones in our prototype narrow beam XLCT. Hence we incorporate the distribution of the X-ray beams in the physical model and collected the optical data from only two perpendicular directions to further speed up the scanning time. Meanwhile we propose a depth related adaptive regularized split Bregman (DARSB) method in reconstruction. The simulation experiments show that the proposed physical model and method can achieve better results in the location error, dice coefficient, mean square error and the intensity error than the traditional split Bregman method and validate the feasibility of method. The phantom experiment can obtain the location error less than 1.1 mm and validate that the incorporation of fan-shaped X-ray beams in our model can achieve better results than the parallel X-rays.

Unsupervised deconvolution of dynamic imaging reveals intratumor vascular heterogeneity and repopulation dynamics

With the existence of biologically distinctive malignant cells originated within the same tumor, intratumor functional heterogeneity is present in many cancers and is often manifested by the intermingled vascular compartments with distinct pharmacokinetics. However, intratumor vascular heterogeneity cannot be resolved directly by most in vivo dynamic imaging. We developed multi-tissue compartment modeling (MTCM), a completely unsupervised method of deconvoluting dynamic imaging series from heterogeneous tumors that can improve vascular characterization in many biological contexts. Applying MTCM to dynamic contrast-enhanced magnetic resonance imaging of breast cancers revealed characteristic intratumor vascular heterogeneity and therapeutic responses that were otherwise undetectable. MTCM is readily applicable to other dynamic imaging modalities for studying intratumor functional and phenotypic heterogeneity, together with a variety of foreseeable applications in the clinic.