Automated Restarting Fast Proximal Gradient Descent Method for Single-View Cone-Beam X-ray Luminescence Computed Tomography Based on Depth Compensation.

Single-view cone-beam X-ray luminescence computed tomography (CB-XLCT) has recently gained attention as a highly promising imaging technique that allows for the efficient and rapid three-dimensional visualization of nanophosphor (NP) distributions in small animals. However, the reconstruction performance is hindered by the ill-posed nature of the inverse problem and the effects of depth variation as only a single view is acquired. To tackle this issue, we present a methodology that integrates an automated restarting strategy with depth compensation to achieve reconstruction. The present study employs a fast proximal gradient descent (FPGD) method, incorporating L0 norm regularization, to achieve efficient reconstruction with accelerated convergence. The proposed approach offers the benefit of retrieving neighboring multitarget distributions without the need for CT priors. Additionally, the automated restarting strategy ensures reliable reconstructions without the need for manual intervention. Numerical simulations and physical phantom experiments were conducted using a custom CB-XLCT system to demonstrate the accuracy of the proposed method in resolving adjacent NPs. The results showed that this method had the lowest relative error compared to other few-view techniques. This study signifies a significant progression in the development of practical single-view CB-XLCT for high-resolution 3-D biomedical imaging.

Multi-targeting oligopyridiniums: Rational design for biofilm dispersion and bacterial persister eradication.

The development of effective antibacterial drugs to combat bacterial infections, particularly the biofilm-related infections, remains a challenge. There are two important features of bacterial biofilms, which are well-known critical factors causing biofilms hard-to-treat in clinical, including the dense and impermeable extracellular polymeric substances (EPS) and the metabolically repressed dormant and persistent bacterial population embedded. These characteristics largely increase the difficulty for regular antibiotic treatment due to insufficient penetration into EPS. In addition, the dormant bacteria are insensitive to the growth-inhibiting mechanism of traditional antibiotics. Herein, we explore the potential of a series of new oligopyridinium-based oligomers bearing a multi-biomacromolecule targeting function as the potent bacterial biofilm eradication agent. These oligomers were rationally designed to be "charge-on-backbone" that can offer a special alternating amphiphilicity. This novel and unique feature endows high affinity to bacterial membrane lipids, DNAs as well as proteins. Such a broad multi-targeting nature of molecules not only enables its penetration into EPS, but also plays vital roles in the bactericidal mechanism of action that is highly effective against dormant and persistent bacteria. Our in vitro, ex vivo, and in vivo studies demonstrated that OPc3, one of the most effective derivatives, was able to offer excellent antibacterial potency against a variety of bacteria and effectively eliminate biofilms in zebrafish models and mouse wound biofilm infection models.

Chemical Biology Approach to Reveal the Importance of Precise Subcellular Targeting for Intracellular Staphylococcus aureus Eradication.

Intracellular bacterial pathogens, such as Staphylococcus aureus, that may hide in intracellular vacuoles represent the most significant manifestation of bacterial persistence. They are critically associated with chronic infections and antibiotic resistance, as conventional antibiotics are ineffective against such intracellular persisters due to permeability issues and mechanistic reasons. Direct subcellular targeting of S. aureus vacuoles suggests an explicit opportunity for the eradication of these persisters, but a comprehensive understanding of the chemical biology nature and significance of precise S. aureus vacuole targeting remains limited. Here, we report an oligoguanidine-based peptidomimetic that effectively targets and eradicates intracellular S. aureus persisters in the phagolysosome lumen, and this oligomer was utilized to reveal the mechanistic insights linking precise targeting to intracellular antimicrobial efficacy. The oligomer has high cellular uptake via a receptor-mediated endocytosis pathway and colocalizes with S. aureus persisters in phagolysosomes as a result of endosome-lysosome interconversion and lysosome-phagosome fusion. Moreover, the observation of a bacterium's altered susceptibility to the oligomer following a modification in its intracellular localization offers direct evidence of the critical importance of precise intracellular targeting. In addition, eradication of intracellular S. aureus persisters was achieved by the oligomer's membrane/DNA dual-targeting mechanism of action; therefore, its effectiveness is not hampered by the hibernation state of the persisters. Such precise subcellular targeting of S. aureus vacuoles also increases the agent's biocompatibility by minimizing its interaction with other organelles, endowing excellent in vivo bacterial targeting and therapeutic efficacy in animal models.

Roles of Phosphorylation of N-Methyl-D-Aspartate Receptor in Chronic Pain.

Phosphorylation of N-methyl-D-aspartate receptor (NMDAR) is widely regarded as a vital modification of synaptic function. Various protein kinases are responsible for direct phosphorylation of NMDAR, such as cyclic adenosine monophosphate-dependent protein kinase A, protein kinase C, Ca2+/calmodulin-dependent protein kinase II, Src family protein tyrosine kinases, cyclin-dependent kinase 5, and casein kinase II. The detailed function of these kinases on distinct subunits of NMDAR has been reported previously and contributes to phosphorylation at sites predominately within the C-terminal of NMDAR. Phosphorylation underlies both structural and functional changes observed in chronic pain, and studies have demonstrated that inhibitors of kinases are significantly effective in alleviating pain behavior in different chronic pain models. In addition, the exploration of drugs that aim to disrupt the interaction between kinases and NMDAR is promising in clinical research. Based on research regarding the modulation of NMDAR in chronic pain models, this review provides an overview of the phosphorylation of NMDAR-related mechanisms underlying chronic pain to elucidate molecular and pharmacologic references for chronic pain management.

Cooperative Membrane Damage as a Mechanism for Pentamidine-Antibiotic Mutual Sensitization.

Most Gram-positive-selective antibiotics have low activity against Gram-negative bacteria due to the presence of an outer membrane barrier. There is, therefore, interest in developing combination therapies that can penetrate the outer membrane (OM) with known antibiotics coupled with membrane-active sensitizing adjuvants. However, two unanswered questions hinder the development of such combination therapies: the sensitization spectrum of the sensitizer and the mechanism of antibiotic-sensitizer mutual potentiation. Here, with pentamidine as an example, we screened a library of 170 FDA-approved antibiotics in combination with pentamidine, a compound known to disturb the OM of Gram-negative bacteria. We found that four antibiotics, minocycline, linezolid, valnemulin, and nadifloxacin, displaced enhanced activity in combination with pentamidine against several multidrug-resistant Gram-negative bacteria. Through a descriptor-based structural-activity analysis and multiple cell-based biochemical assays, we found that hydrophobicity, partial charge, rigidity, and surface rugosity were key factors that affected sensitization via a cooperative membrane damage mechanism in which lipopolysaccharides and phospholipids were identified as sites of synergy. Finally, in vitro experiments showed that the linezolid-pentamidine combination slowed the generation of drug resistance, and there was also potent activity in in vivo experiments. Overall, our results highlight the importance of the physicochemical properties of antibiotics and cooperative membrane damage for synergistic pentamidine-antibiotic drug combinations.

Prediction of EGFR Mutation Status in Non-Small Cell Lung Cancer Based on Ensemble Learning.

Objectives: We aimed to identify whether ensemble learning can improve the performance of the epidermal growth factor receptor (EGFR) mutation status predicting model. Methods: We retrospectively collected 168 patients with non-small cell lung cancer (NSCLC), who underwent both computed tomography (CT) examination and EGFR test. Using the radiomics features extracted from the CT images, an ensemble model was established with four individual classifiers: logistic regression (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). The synthetic minority oversampling technique (SMOTE) was also used to decrease the influence of data imbalance. The performances of the predicting model were evaluated using the area under the curve (AUC). Results: Based on the 26 radiomics features after feature selection, the SVM performed best (AUCs of 0.8634 and 0.7885 on the training and test sets, respectively) among four individual classifiers. The ensemble model of RF, XGBoost, and LR achieved the best performance (AUCs of 0.8465 and 0.8654 on the training and test sets, respectively). Conclusion: Ensemble learning can improve the model performance in predicting the EGFR mutation status of patients with NSCLC, showing potential value in clinical practice.

Improving the Hemocompatibility of Antimicrobial Peptidomimetics through Amphiphilicity Masking Using a Secondary Amphiphilic Polymer.

Antimicrobial peptidomimetics (AMPMs) have received widespread attention as potentially powerful weapons against antibiotic resistance. However, AMPMs' membrane disruption mechanism not only brings resistance-resistant nature, but also nonspecific binding and disruption toward eukaryotic cell membranes, and consequently, their hemolytic activity is the primary concern on clinical applications. Here, the preparation and screening of an AMPM library is reported, through which a best-performing hit, PT-b1, can be obtained. To further improve PT-b1's hemocompatibility, a strategy is devised to mask the amphiphilicity of the AMPM using a charge-free, FDA-approved amphiphilic polymer, Pluronic F-127 (PF127). A PF127 solution containing PT-b1 can form a temperature-sensitive, absorbable hydrogel at higher concentration, but dissolve and complex with PT-b1 through hydrophobic interactions at lower concentration or lower temperature. The complexation from PF127 can mask the amphiphilicity of PT-b1 and render it extremely hemocompatible, yet the reversibility in such nanocomplexation and the existence of a secondary mechanism of action ensure that the AMPM's potency remains unchanged. The in vivo effectiveness of this antimicrobial hydrogel system is demonstrated using a mice wound infection model established with Methicillin-resistant Staphylococcus aureus, and observations indicate the hydrogel can promote wound healing and suppress bacteria-caused inflammation even when resistant pathogens are involved.

PIxel-Level Segmentation of Bladder Tumors on MR Images Using a Random Forest Classifier.

Objectives: Regional bladder wall thickening on noninvasive magnetic resonance (MR) images is an important sign of developing urinary bladder cancer (BCa), and precise segmentation of the tumor mass is an essential step toward noninvasive identification of the pathological stage and grade, which is of critical importance for the clinical management of patients with BCa. Methods: In this paper, we proposed a new method based on the high-throughput pixel-level features and a random forest (RF) classifier for the BCa segmentation. First, regions of interest (ROIs) including tumor and wall ROIs were used in the training set for feature extraction and segmentation model development. Then, candidate regions containing both bladder tumor and its neighboring wall tissue in the testing set were segmented. Results: Experimental results were evaluated on a retrospective database containing 56 patients postoperatively confirmed with BCa from the affiliated hospital. The Dice similarity coefficient (DSC) and average symmetric surface distance (ASSD) of the tumor regions were adopted to quantitatively assess the overall performance of this approach. The results showed that the mean DSC was 0.906 (95% confidential interval [CI]: 0.852-0.959), and the mean ASSD was 1.190 mm (95% CI: 1.727-2.449), which were higher than those of the state-of-the-art methods for tumor region separation. Conclusion: The proposed Pixel-level BCa segmentation method can achieve good performance for the accurate segmentation of BCa lesion on MR images.

TRAF6 promotes IL-4-induced M2 macrophage activation by stabilizing STAT6.

E3 ligase TRAF6 plays a critical role in TLRs trigged M1 macrophage activation. However, the function of TRAF6 in IL-4-induced M2 macrophage activation has not been illuminated. We report here that deficiency of TRAF6 significantly impaired IL-4-induced genes expression in macrophage. Mechanistically, TRAF6 mediated the protein stability of STAT6, a key factor in IL-4 signaling. Overexpression of TRAF6 increased STAT6 protein level, conversely, knockdown or knockout of endogenous TRAF6 decreased it. Further study showed that TRAF6 bound STAT6 by TRAF6 C domain and reduced K48-ubiquitination of STAT6 which could induce degradation of STAT6, explaining why TRAF6 could conduct STAT6 stability. Intriguingly, the E3 ligase activity of TRAF6 was dispensable for stabilizing STAT6, despite TRAF6 promoted its K63 ubiquitination. These results indicate that TRAF6 is essential for STAT6 stability in IL-4 signaling and may act as a positive regulator in both M1 and M2 polarization.

Principal Component Analysis Based Dynamic Cone Beam X-Ray Luminescence Computed Tomography: A Feasibility Study.

Cone beam X-ray luminescence computed tomography (CB-XLCT) is a promising imaging technique in studying the physiological and pathological processes in small animals. However, the dynamic bio-distributions of probes in small animal, especially in adjacent targets are still hard to be captured directly from dynamic CB-XLCT. In this paper, a 4D temporal-spatial reconstruction method based on principal component analysis (PCA) in the projection space is proposed for dynamic CB-XLCT. First, projections of angles in each 3D frame are compressed to reduce the noises initially. Then a temporal PCA is performed on the projection data to decorrelate the 4D problem into separate 3D problems in the PCA domain. In the PCA domain, the difference between dynamic behaviors of multiple targets can be reflected by the first several principal components which can be further used for fast and improved reconstruction by a restarted Tikhonov regularization method. At last, by discarding the principal components mainly reflecting noise, the concentration series of targets are recovered from the first few reconstruction results with a mask as the constraint. The numerical simulation and phantom experiment demonstrate that the proposed method can resolve multiple targets and recover the dynamic distributions with high computation efficiency. The proposed method provides new feasibility for imaging dynamic bio-distributions of probes in vivo.

Regularized reconstruction based on joint L1 and total variation for sparse-view cone-beam X-ray luminescence computed tomography.

As an emerging hybrid imaging modality, cone-beam X-ray luminescence computed tomography (CB-XLCT) has been proposed based on the development of X-ray excitable nanoparticles. Owing to the high degree of absorption and scattering of light through tissues, the CB-XLCT inverse problem is inherently ill-conditioned. Appropriate priors or regularizations are needed to facilitate reconstruction and to restrict the search space to a specific solution set. Typically, the goal of CB-XLCT reconstruction is to get the distributions of nanophosphors in the imaging object. Considering that the distributions of nanophosphors inside bodies preferentially accumulate in specific areas of interest, the reconstruction of XLCT images is usually sparse with some locally smoothed high-intensity regions. Therefore, a combination of the L1 and total variation regularization is designed to improve the imaging quality of CB-XLCT in this study. The L1 regularization is used for enforcing the sparsity of the reconstructed images and the total variation regularization is used for maintaining the local smoothness of the reconstructed image. The implementation of this method can be divided into two parts. First, the reconstruction image was reconstructed based on the fast iterative shrinkage-thresholding (FISTA) algorithm, then the reconstruction image was minimized by the gradient descent method. Numerical simulations and phantom experiments indicate that compared with the traditional ART, ADAPTIK and FISTA methods, the proposed method demonstrates its advantage in improving spatial resolution and reducing imaging time.

Spectral-resolved cone-beam X-ray luminescence computed tomography with principle component analysis.

Cone-beam X-ray luminescence computed tomography (CB-XLCT) has become a promising technique for its higher utilization of X-ray and shorter scanning time compared to the narrow-beam XLCT, but it suffers from the low-spatial resolution that results in the insufficiency to resolve the adjacent multiple probes. In multispectral CB-XLCT, multiple probes show different emission behaviors in the dimension of the spectrum. In this work, a spectral-resolved CB-XLCT method combining multispectral CB-XLCT with principle component analysis (PCA) was proposed to improve the imaging resolution. Results of digital simulation and the phantom experiment illustrated that the proposed method was capable of resolving adjacent multiple probes accurately and had better performance than the common multispectral CB-XLCT with spectrum information priori.

Sparse view cone beam X-ray luminescence tomography based on truncated singular value decomposition.

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 L1-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.

Sub-10 nm Water-Dispersible ß-NaGdF4:X% Eu3+ Nanoparticles with Enhanced Biocompatibility for in Vivo X-ray Luminescence Computed Tomography.

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 ß-NaGdF4:X% Eu3+ 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 (ß-) NaGdF4 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 Eu3+ ions doped into NaGdF4 host. Under X-ray excitation, the ß-NaGdF4 nanoparticles with a doping level of 15% Eu3+ exhibited the most efficient luminescence intensity. Notably, the doping level of Eu3+ has no effect on the crystal phase and morphology of the NaGdF4-based host. Afterward, ß-NaGdF4:15% Eu3+ 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 Y2O3:15% Eu3+. 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.

Resolving adjacent nanophosphors of different concentrations by excitation-based cone-beam X-ray luminescence tomography.

Cone-beam X-ray luminescence computed tomography (CB-XLCT) has been proposed as a new molecular imaging modality recently. It can obtain both anatomical and functional tomographic images of an object efficiently, with the excitation of nanophosphors in vivo or in vitro by cone-beam X-rays. However, the ill-posedness of the CB-XLCT inverse problem degrades the image quality and makes it difficult to resolve adjacent luminescent targets with different concentrations, which is essential in the monitoring of nanoparticle metabolism and drug delivery. To address this problem, a multi-voltage excitation imaging scheme combined with principal component analysis is proposed in this study. Imaging experiments performed on physical phantoms by a custom-made CB-XLCT system demonstrate that two adjacent targets, with different concentrations and an edge-to-edge distance of 0 mm, can be effectively resolved.

Unmixing multiple adjacent fluorescent targets with multispectral excited fluorescence molecular tomography.

Fluorescence molecular tomography (FMT) can visualize biological activities at cellular and molecular levels in vivo, and has been extensively used in drug delivery and tumor detection research of small animals. The ill-posedness of the FMT inverse problem makes it difficult to reconstruct and unmix multiple adjacent fluorescent targets that have different functional features but are labeled with the same fluorochrome. A method based on independent component analysis for multispectral excited FMT was proposed in our previous study. It showed that double fluorescent targets with certain edge-to-edge distance (EED) could be unmixed by the method. In this study, the situation is promoted to unmix multiple adjacent fluorescent targets (i.e., more than two fluorescent targets and EED=0). Phantom experiments on the resolving ability of the proposed algorithm demonstrate that the algorithm performs well in unmixing multiple adjacent fluorescent targets in both lateral and axial directions. And also, we recovered the locational information of each independent fluorescent target and described the variable trends of the corresponding fluorescent targets under the excitation spectrum. This method is capable of unmixing multiple fluorescent targets with small EED but labeled with the same fluorochrome, and may be used in imaging of nonspecific probe targeting and metabolism of drugs.

Perfusion Deficits and Functional Connectivity Alterations in Memory-Related Regions of Patients with Post-Traumatic Stress Disorder.

To explore the potential alterations in cerebral blood flow (CBF) and functional connectivity of recent onset post-traumatic stress disorder (PTSD) induced by a single prolonged trauma exposure, we recruited 20 survivors experiencing the same coal mining flood disaster as the PTSD (n = 10) and non-PTSD (n = 10) group, respectively. The pulsed arterial spin labeling (ASL) images were acquired with a 3.0T MRI scanner and the partial volume (PV) effect in the images was corrected for better CBF estimation. Alterations in CBF were analyzed using both uncorrected and PV-corrected CBF maps. By using altered CBF regions as regions-of-interest, seed-based functional connectivity analysis was then performed. While only one CBF deficit in right corpus callosum of PTSD patients was detected using uncorrected CBF, three more regions (bilateral frontal lobes and right superior frontal gyrus) were identified using PV-corrected CBF. Furthermore, the regional CBF of right superior frontal gyrus exhibited significantly negative correlation with the symptom severity (r = -0.759, p = 0.018). The resting-state functional connectivity analysis revealed increased connectivity between left frontal lobe and right parietal lobe. The results indicated the symptom-specific perfusion deficits and an aberrant connectivity in memory-related regions of PTSD patients when using PV-corrected ASL data. It also suggested that PV-corrected CBF exhibits more subtle changes that may be beneficial to perfusion and connectivity analysis.

Acceleration of dynamic fluorescence molecular tomography with principal component analysis.

Dynamic fluorescence molecular tomography (FMT) is an attractive imaging technique for three-dimensionally resolving the metabolic process of fluorescent biomarkers in small animal. When combined with compartmental modeling, dynamic FMT can be used to obtain parametric images which can provide quantitative pharmacokinetic information for drug development and metabolic research. However, the computational burden of dynamic FMT is extremely huge due to its large data sets arising from the long measurement process and the densely sampling device. In this work, we propose to accelerate the reconstruction process of dynamic FMT based on principal component analysis (PCA). Taking advantage of the compression property of PCA, the dimension of the sub weight matrix used for solving the inverse problem is reduced by retaining only a few principal components which can retain most of the effective information of the sub weight matrix. Therefore, the reconstruction process of dynamic FMT can be accelerated by solving the smaller scale inverse problem. Numerical simulation and mouse experiment are performed to validate the performance of the proposed method. Results show that the proposed method can greatly accelerate the reconstruction of parametric images in dynamic FMT almost without degradation in image quality.

Bayesian Framework Based Direct Reconstruction of Fluorescence Parametric Images.

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.

An adaptive support driven reweighted L1-regularization algorithm for fluorescence molecular tomography.

Fluorescence molecular tomography (FMT) is a promising in vivo functional imaging modality in preclinical study. When solving the ill-posed FMT inverse problem, L1 regularization can preserve the details and reduce the noise in the reconstruction results effectively. Moreover, compared with the regular L1 regularization, reweighted L1 regularization is recently reported to improve the performance. In order to realize the reweighted L1 regularization for FMT, an adaptive support driven reweighted L1-regularization (ASDR-L1) algorithm is proposed in this work. This algorithm has two integral parts: an adaptive support estimate and the iteratively updated weights. In the iteratively reweighted L1-minimization sub-problem, different weights are equivalent to different regularization parameters at different locations. Thus, ASDR-L1 can be considered as a kind of spatially variant regularization methods for FMT. Physical phantom and in vivo mouse experiments were performed to validate the proposed algorithm. The results demonstrate that the proposed reweighted L1-reguarization algorithm can significantly improve the performance in terms of relative quantitation and spatial resolution.