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X-ray luminescence computed tomography (XLCT) uses external X-rays for luminescence excitation, which is becoming a promising molecular imaging technique with superb penetration depth and spatial resolution. To achieve the tomographic mapping of luminescence distribution, accurate optical propagation model and suitable reconstruction method are two keys for XLCT, but not satisfied. To overcome the limitation of the single proton propagation model (e.g., DE, SP 3 ), we adopted a hybrid diffusion equation with third order simplified spherical harmonics (DE-SP 3 ) model for XLCT. To enable fast iteration and accurate sparse reconstruction, we also integrated in the inversion optimization, with a novel Least Square QR-factorization based on the Lasso (Lasso-LSQR) algorithm. We first simulated the light propagation in various kinds of organs under DE model and SP 3 model, respectively. By comparison with the Monte Carlo, these tissues can be categorized into two types, namely DE-fitted tissues that include muscle and lung, and SP 3 -fitted tissues including heart, kidney, liver, and stomach. According to the above classification results, we built a hybrid DE-SP 3 model to more accurately describing light transport. Numerical simulations and in vivo experiments illustrated that hybrid DE-SP 3 model achieves superior reconstruction performance in terms of location accuracy, and spatial resolution than DE, and less computational cost than SP 3 . The hybrid DE-SP 3 model materializes a balance between accuracy and efficiency for XLCT., (© 2021 Wiley-VCH GmbH.)

Objective . X-ray luminescence computed tomography (XLCT) has played a crucial role in pre-clinical research and effective diagnosis of disease. However, due to the ill-posed of the XLCT inverse problem, the generalization of reconstruction methods and the selection of appropriate regularization parameters are still challenging in practical applications. In this research, an robust Elastic net-ℓ 1 ℓ 2 reconstruction method is proposed aiming to the challenge. Approach . Firstly, our approach consists of ℓ 1 and ℓ 2 regularization to enhance the sparsity and suppress the smoothness. Secondly, through optimal approximation of the optimization problem, double modification of Landweber algorithm is adopted to solve the Elastic net-ℓ 1 ℓ 2 regulazation. Thirdly, drawing on the ideal of supervised learning, multi-parameter K-fold cross validation strategy is proposed to determin the optimal parameters adaptively. Main results . To evaluate the performance of the Elastic net-ℓ 1 ℓ 2 method, numerical simulations, phantom and in vivo experiments were conducted. In these experiments, the Elastic net-ℓ 1 ℓ 2 method achieved the minimum reconstruction error (with smallest location error, fluorescent yield relative error, normalized root-mean-square error) and the best image reconstruction quality (with largest contrast-to-noise ratio and Dice similarity) among all methods. The results demonstrated that Elastic net-ℓ 1 ℓ 2 can obtain superior reconstruction performance in terms of location accuracy, dual source resolution, robustness and in vivo practicability. Significance . It is believed that this study will further benefit preclinical applications with a view to provide a more reliable reference for the later researches on XLCT., (© 2021 Institute of Physics and Engineering in Medicine.)

Bioluminescence tomography (BLT) has important applications in the in vivo visualization of a pathological process for preclinical studies. However, the reconstruction of BLT is severely ill-posed. To recover the bioluminescence source stably and efficiently, we use a log-sum regularization term in the objective function and utilize a hybrid optimization algorithm for solving the nonconvex regularized problems (HONOR). The hybrid optimization scheme of HONOR merges second-order information and first-order information to reconstruction by choosing either the quasi-Newton (QN) or gradient descent step at each iteration. The QN step uses the limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm (L-BFGS) to acquire second-order information. Simulations and in vivo experiments based on multispectral measurements demonstrated the remarkable performance of the proposed hybrid method in the sparse reconstruction of BLT.

Bioluminescence tomography is a preclinical imaging modality to locate and quantify internal bioluminescent sources from surface measurements, which experienced rapid growth in the last 10 years. However, multiple-source resolving remains a challenging issue in BLT. In this study, it is treated as an unsupervised pattern recognition problem based on the reconstruction result, and a novel hybrid clustering algorithm combining the advantages of affinity propagation (AP) and K-means is developed to identify multiple sources automatically. Moreover, we incorporate the clustering analysis into a general multiple-source reconstruction framework, which can provide stable reconstruction and accurate resolving result without providing the number of targets. Numerical simulations and in vivo experiments on 4T1-luc2 mouse model were conducted to assess the performance of the proposed method in multiple-source resolving. The encouraging results demonstrate significant effectiveness and potential of our method in preclinical BLT applications., (© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Cerenkov luminescence tomography (CLT) is a promising non-invasive optical imaging method with three-dimensional semiquantitative in vivo imaging capability. However, CLT itself relies on Cerenkov radiation, a low-intensity radiation, making CLT reconstruction more challenging than other imaging modalities. In order to solve the ill-posed inverse problem of CLT imaging, some numerical optimization or regularization methods need to be applied. However, in commonly used methods for solving inverse problems, parameter selection significantly influences the results. Therefore, this paper proposed a probabilistic energy distribution density region scaling (P-EDDRS) framework. In this framework, multiple reconstruction iterations are performed, and the Cerenkov source distribution of each reconstruction is treated as random variables. According to the spatial energy distribution density, the new region of interest (ROI) is solved. The size of the region required for the next operation was determined dynamically by combining the intensity characteristics. In addition, each reconstruction source distribution is given a probability weight value, and the prior probability in the subsequent reconstruction is refreshed. Last, all the reconstruction source distributions are weighted with the corresponding probability weights to get the final Cerenkov source distribution. To evaluate the performance of the P-EDDRS framework in CLT, this article performed numerical simulation, in vivo pseudotumor model mouse experiment, and breast cancer mouse experiment. Experimental results show that this reconstruction framework has better positioning accuracy and shape recovery ability and can optimize the reconstruction effect of multiple algorithms on CLT. [ABSTRACT FROM AUTHOR]

Cerenkov luminescence tomography (CLT) has attracted much attention because of the wide clinically-used probes and three-dimensional (3D) quantification ability. However, due to the serious morbidity of 3D optical imaging, the reconstructed images of CLT are not appreciable, especially when single-view measurements are used. Single-view CLT improves the efficiency of data acquisition. It is much consistent with the actual imaging environment of using commercial imaging system, but bringing the problem that the reconstructed results will be closer to the animal surface on the side where the single-view image is collected. To avoid this problem to the greatest extent possible, we proposed a prior compensation algorithm for CLT reconstruction based on depth calibration strategy. This method takes full account of the fact that the attenuation of light in the tissue will depend heavily on the depth of the light source as well as the distance between the light source and the detection plane. Based on this consideration, a depth calibration matrix was designed to calibrate the attenuation between the surface light flux and the density of the internal light source. The feature of the algorithm was that the depth calibration matrix directly acts on the system matrix of CLT reconstruction, rather than modifying the regularization penalty items. The validity and effectiveness of the proposed algorithm were evaluated with a numerical simulation and a mouse-based experiment, whose results illustrated that it located the radiation sources accurately by using single-view measurements. [ABSTRACT FROM AUTHOR]

In this paper, spherical carbon dots (CDs) with distinct compositions and surface states have been successfully synthesized by a facile microwave method. From the fluorescence spectra, several characteristic luminescence features have been observed: surface amino groups are dominant in the whole emission spectra centering at 445 nm, and the fingerprint emissions relevant to the impurity levels formed by some groups related to C and N elements, including C-C/C=C (intrinsic C), C-N (graphitic N), N-containing heterocycles (pyridine N) and C=O groups, are located around 305 nm, 355 nm, 410 nm, and 500 nm, respectively. Those fine luminescence features could be ascribed to the electron transition among various trapping states within the band structure caused by different chemical bonds in carbon cores, or functional groups attached to the CDs’ surfaces. According to the theoretical calculations and experimental results, a scheme of the band structure has been proposed to describe the positions of those trapping states within the band gap. Additionally, it has also been observed that the emission of CDs is sensitive to the concentration of Fe3+ ions with a linear relation in the range of Fe3+ concentration from 12.5 to 250 μM. [ABSTRACT FROM AUTHOR]

Boron carbon oxynitride quantum dots (BCNO QDs) with blue emission were prepared via the template of SBA–15 (a typical mesoporous silica). A modulated photoluminescence sensor was developed based on the different quenching effects of Cu 2+ or Hg 2+ ions on the luminescence intensity of BCNO QDs. The Cu 2+ or Hg 2+ ions have an interaction with BCNO QDs due to the electrons transfer between the BCNO and Cu 2+ or Hg 2+ ions, and the detection limit of Cu 2+ or Hg 2+ ion concentration can be as less as 10 nM. The BCNO–Cr 6+ mixture can be served as a turn–on fluorescent sensor for detecting the ascorbic acid based on the inner filter effect since overlapping of excitation and emission spectra between Cr 6+ ions and BCNO QDs. Moreover, the BCNO QDs can also be applied to fingerprint identification and organic fluorescent films under ultraviolet excitation. [ABSTRACT FROM AUTHOR]

We prepared BCNO phosphors with yellow and orange emission and investigated the effects of chemical bonds on luminescence and absorption properties for BCNO phosphors in solid phase, water, acid and alkaline solutions. The BCNO phosphors are mainly consisted of B‒N, C‒N, B‒O, C˭C, C‒H and N‒H chemical bonds, and the intensity and content of chemical bonds are related to the ratio of boric acid to glycine. The emission peaks of BCNO phosphors are red shifted from 559 nm to 605 nm with increasing ratio of boric acid to glycine (1:1 to 1:10). The range of excitation and absorption spectra are broadened with increased ratio of boric acid to glycine. In addition, the acid and alkaline solutions have different effects on excitation, emission and absorption spectra of BCNO phosphors. The changes of luminescence and absorption properties of BCNO phosphors are related to the regulation of surface states and chemical binding status of BCNO phosphors, which is originated from the variety of relative energy band structure. [ABSTRACT FROM AUTHOR]

• LSPR Effects of Ag NPs on luminescence of CDs are systematically investigated. • Luminescence intensity of CDs can be improved 5–6 times by LSPR of Ag@SiO 2 NPs. • Fluorescence quenching and enhancement mechanism is clarified between CDs and Ag NPs. We disclose the effects of localized surface plasmon resonance (LSPR) of Ag nanoparticles (NPs) on luminescence properties of carbon dots (CDs) with blue, green and yellow emission, respectively. The Ag NPs without coating layer result in fluorescence quenching of CDs with blue, green and yellow emissions. The Ag NPs coated with SiO 2 layer only quench the blue emitting CDs (B-CDs) since the overlap of emission and LSPR absorption of Ag NPs. However, the fluorescence enhancement of CDs with green (G-CDs) and yellow emission (Y-CDs) can be realized by adjusting the size of the Ag NPs and the distance between Ag NPs and CDs via silica coating (Ag@SiO 2). The luminescence intensity of G-CDs and Y-CDs can be improved 5–6 times as the coating thickness of SiO 2 is 15 nm. Fluorescence quenching and enhancement mechanism can be clarified by the electron transition and energy transfer process between CDs and Ag NPs, which provides guidance principle for LSPR enhanced fluorescence. [ABSTRACT FROM AUTHOR]