Articles you may be interested inUltrasound guided fluorescence molecular tomography with improved quantification by an attenuation compensated born-normalization and in vivo preclinical study of cancer Rev. Sci. Instrum. 85, 053703 (2014); 10.1063/1.4875256 A generic, geometric cocalibration method for a combined system of fluorescence molecular tomography and microcomputed tomography with arbitrarily shaped objects Med.

As a novel molecular and functional imaging modality, X-ray luminescence computed tomography (XLCT) has shown its potentials in biomedical and preclinic applications. However, there are still some limitations of X-ray-excited luminescent materials, such as low luminescence efficiency, poor biocompatibility, and cytotoxicity, making in vivo XLCT imaging quite challenging. In this study, for the very first time, we present on using sub-10 nm β-NaGdF:X% Eu nanoparticles with poly(acrylic acid) (PAA) surface modification, which demonstrate outstanding luminescence efficiency, uniform size distribution, water dispersity, and biosafety, as the luminescent probes for in vivo XLCT application. The pure hexagonal phase (β-) NaGdF has been successfully synthesized and characterized by X-ray powder diffraction (XRD) and transmission electron microscopy (TEM), and then the results of X-ray photoelectron spectroscopy (XPS), energy-dispersive X-ray spectrometry  (EDX), and elemental mapping further confirm Eu ions doped into NaGdF host. Under X-ray excitation, the β-NaGdF nanoparticles with a doping level of 15% Eu exhibited the most efficient luminescence intensity. Notably, the doping level of Eu has no effect on the crystal phase and morphology of the NaGdF-based host. Afterward, β-NaGdF:15% Eu nanoparticles were modified with PAA to enhance the water dispersity and biocompatibility. The compatibility of in vivo XLCT imaging using such nanoparticles was systematically studied via in vitro cytotoxicity, physical phantom, and in vivo imaging experiments. The ultralow cytotoxicity of PAA-modified nanoparticles, which is confirmed by over 80% cell viability of SH-SY5Y cells when treated by high nanoparticle concentration of 200 μg/mL, overcome the major obstacle for in vivo application. In addition, the high luminescence intensity of PAA-modified nanoparticles enables the location error of in vivo XLCT imaging less than 2 mm, which is comparable to that using commercially available bulk material YO:15% Eu. The proposed nanoparticles promote XLCT research into an in vivo stage. Further modification of these nanoparticles with biofunctional molecules could enable the potential of targeting XLCT imaging.

Fluorescence imaging has been successfully used in the study of pharmacokinetic analysis, while dynamic fluorescence molecular tomography (FMT) is an attractive imaging technique for three-dimensionally resolving the metabolic process of fluorescent biomarkers in small animals in vivo. Parametric images obtained by combining dynamic FMT with compartmental modeling can provide quantitative physiological information for biological studies and drug development. However, images obtained with conventional indirect methods suffer from poor image quality because of failure in utilizing the temporal correlations of boundary measurements. Besides, FMT suffers from low spatial resolution due to its ill-posed nature, which further reduces the image quality. In this paper, we propose a novel method to directly reconstruct parametric images from boundary measurements based on maximum a posteriori (MAP) estimation with structural priors in a Bayesian framework. The proposed method can utilize structural priors obtained from an X-ray computed tomography system to mitigate the ill-posedness of dynamic FMT inverse problem, and use direct reconstruction strategy to make full use of temporal correlations of boundary measurements. The results of numerical simulations and in vivo mouse experiments demonstrate that the proposed method leads to significant improvements in the reconstruction quality of parametric images as compared with the conventional indirect method and a previously developed direct method.

Cone beam X-ray luminescence computed tomography (CB-XLCT) has been proposed as a promising hybrid imaging technique. Though it has the advantage of fast imaging, the inverse problem of CB-XLCT is seriously ill-conditioned, making the image quality quite poor, especially for imaging multi-targets. To achieve fast imaging of multi-targets, which is essential for in vivo applications, a truncated singular value decomposition (TSVD) based sparse view CB-XLCT reconstruction method is proposed in this study. With the weight matrix of the CB-XLCT system being converted to orthogonal by TSVD, the compressed sensing (CS) based L-norm method could be applied for fast reconstruction from fewer projection views. Numerical simulations and phantom experiments demonstrate that by using the proposed method, two targets with different edge-to-edge distances (EEDs) could be resolved effectively. It indicates that the proposed method could improve the imaging quality of multi-targets significantly in terms of localization accuracy, target shape, image contrast, and spatial resolution, when compared with conventional methods.

Images of pharmacokinetic parameters in dynamic fluorescence molecular tomography (FMT) have the potential to provide quantitative physiological information for biological studies and drug development. However, images obtained with conventional indirect methods suffer from low signal-to-noise ratio because of failure in efficiently modeling the measurement noise. Besides, FMT suffers from low spatial resolution due to its ill-posed nature, which further reduces the image quality. In this letter, we present a direct method with structural priors for imaging pharmacokinetic parameters, which uses a nonlinear objective function to efficiently model the measurement noise and utilizes the structural priors to mitigate the ill-posedness of FMT. The results of numerical simulations and in vivo mouse experiments demonstrate that the proposed method leads to significant improvements in the image quality.