Fluorescence molecular tomography in the second near-infrared window

Projected algebraic reconstruction technique-network for high-fidelity diffuse fluorescence tomography reconstruction

We propose a model-driven projected algebraic reconstruction technique (PART)-network (PART-Net) that leverages the advantages of the traditional model-based method and the neural network to improve the imaging quality of diffuse fluorescence tomography. In this algorithm, nonnegative prior information is incorporated into the ART iteration process to better guide the optimization process, and thereby improve imaging quality. On this basis, PART in conjunction with a residual convolutional neural network is further proposed to obtain high-fidelity image reconstruction. The numerical simulation results demonstrate that the PART-Net algorithm effectively improves noise robustness and reconstruction accuracy by at least 1-2 times and exhibits superiority in spatial resolution and quantification, especially for a small-sized target (r=2m m), compared with the traditional ART algorithm. Furthermore, the phantom and in vivo experiments verify the effectiveness of the PART-Net, suggesting strong generalization capability and a great potential for practical applications.

Multi-target reconstruction strategy based on blind source separation of surface measurement signals in FMT

Fluorescence molecular tomography (FMT) is a promising molecular imaging technique for tumor detection in the early stage. High-precision multi-target reconstructions are necessary for quantitative analysis in practical FMT applications. The existing reconstruction methods perform well in retrieving a single fluorescent target but may fail in reconstructing a multi-target, which remains an obstacle to the wider application of FMT. In this paper, a novel multi-target reconstruction strategy based on blind source separation (BSS) of surface measurement signals was proposed, which transformed the multi-target reconstruction problem into multiple single-target reconstruction problems. Firstly, by multiple points excitation, multiple groups of superimposed measurement signals conforming to the conditions of BSS were constructed. Secondly, an efficient nonnegative least-correlated component analysis with iterative volume maximization (nLCA-IVM) algorithm was applied to construct the separation matrix, and the superimposed measurement signals were separated into the measurements of each target. Thirdly, the least squares fitting method was combined with BSS to determine the number of fluorophores indirectly. Lastly, each target was reconstructed based on the extracted surface measurement signals. Numerical simulations and in vivo experiments proved that it has the ability of multi-target resolution for FMT. The encouraging results demonstrate the significant effectiveness and potential of our method for practical FMT applications.

Practical Guidance for Developing Small-Molecule Optical Probes for In Vivo Imaging

The WMIS Education Committee (2019-2022) reached a consensus that white papers on molecular imaging could be beneficial for practitioners of molecular imaging at their early career stages and other scientists who are interested in molecular imaging. With this consensus, the committee plans to publish a series of white papers on topics related to the daily practice of molecular imaging. In this white paper, we aim to provide practical guidance that could be helpful for optical molecular imaging, particularly for small molecule probe development and validation in vitro and in vivo. The focus of this paper is preclinical animal studies with small-molecule optical probes. Near-infrared fluorescence imaging, bioluminescence imaging, chemiluminescence imaging, image-guided surgery, and Cerenkov luminescence imaging are discussed in this white paper.

Excitation-based fully connected network for precise NIR-II fluorescence molecular tomography

Fluorescence molecular tomography (FMT) is a novel imaging modality to obtain fluorescence biomarkers' three-dimensional (3D) distribution. However, the simplified mathematical model and complicated inverse problem limit it to achieving precise results. In this study, the second near-infrared (NIR-II) fluorescence imaging was adopted to mitigate tissue scattering and reduce noise interference. An excitation-based fully connected network was proposed to model the inverse process of NIR-II photon propagation and directly obtain the 3D distribution of the light source. An excitation block was embedded in the network allowing it to autonomously pay more attention to neurons related to the light source. The barycenter error was added to the loss function to improve the localization accuracy of the light source. Both numerical simulation and in vivo experiments showed the superiority of the novel NIR-II FMT reconstruction strategy over the baseline methods. This strategy was expected to facilitate the application of machine learning in biomedical research.

Multi-branch attention prior based parameterized generative adversarial network for fast and accurate limited-projection reconstruction in fluorescence molecular tomography

Limited-projection fluorescence molecular tomography (FMT) allows rapid reconstruction of the three-dimensional (3D) distribution of fluorescent targets within a shorter data acquisition time. However, the limited-projection FMT is severely ill-posed and ill-conditioned due to insufficient fluorescence measurements and the strong scattering properties of photons in biological tissues. Previously, regularization-based methods, combined with the sparse distribution of fluorescent sources, have been commonly used to alleviate the severe ill-posed nature of the limited-projection FMT. Due to the complex iterative computations, time-consuming solution procedures, and less stable reconstruction results, the limited-projection FMT remains an intractable challenge for achieving fast and accurate reconstructions. In this work, we completely discard the previous iterative solving-based reconstruction themes and propose multi-branch attention prior based parameterized generative adversarial network (MAP-PGAN) to achieve fast and accurate limited-projection FMT reconstruction. Firstly, the multi-branch attention can provide parameterized weighted sparse prior information for fluorescent sources, enabling MAP-PGAN to effectively mitigate the ill-posedness and significantly improve the reconstruction accuracy of limited-projection FMT. Secondly, since the end-to-end direct reconstruction strategy is adopted, the complex iterative computation process in traditional regularization algorithms can be avoided, thus greatly accelerating the 3D visualization process. The numerical simulation results show that the proposed MAP-PGAN method outperforms the state-of-the-art methods in terms of localization accuracy and morphological recovery. Meanwhile, the reconstruction time is only about 0.18s, which is about 100 to 1000 times faster than the conventional iteration-based regularization algorithms. The reconstruction results from the physical phantoms and in vivo experiments further demonstrate the feasibility and practicality of the MAP-PGAN method in achieving fast and accurate limited-projection FMT reconstruction.

A review of advances in imaging methodology in fluorescence molecular tomography

Objective. Fluorescence molecular tomography (FMT) is a promising non-invasive optical molecular imaging technology with strong specificity and sensitivity that has great potential for preclinical and clinical studies in tumor diagnosis, drug development and therapeutic evaluation. However, the strong scattering of photons and insufficient surface measurements make it very challenging to improve the quality of FMT image reconstruction and its practical application for early tumor detection. Therefore, continuous efforts have been made to explore more effective approaches or solutions in the pursuit of high-quality FMT reconstructions. Approach. This review takes a comprehensive overview of advances in imaging methodology for FMT, mainly focusing on two critical issues in FMT reconstructions: improving the accuracy of solving the forward physical model and mitigating the ill-posed nature of the inverse problem from a methodological point of view. More importantly, numerous impressive and practical strategies and methods for improving the quality of FMT reconstruction are summarized. Notably, deep learning methods are discussed in detail to illustrate their advantages in promoting the imaging performance of FMT thanks to large datasets, the emergence of optimized algorithms and the application of innovative networks. Main results. The results demonstrate that the imaging quality of FMT can be effectively promoted by improving the accuracy of optical parameter modeling, combined with prior knowledge, and reducing dimensionality. In addition, the traditional regularization-based methods and deep neural network-based methods, especially end-to-end deep networks, can enormously alleviate the ill-posedness of the inverse problem and improve the quality of FMT image reconstruction. Significance. This review aims to illustrate a variety of effective and practical methods for the reconstruction of FMT images that may benefit future research. Furthermore, it may provide some valuable research ideas and directions for FMT in the future, and could promote, to a certain extent, the development of FMT and other methods of optical tomography.

UHR-DeepFMT: Ultra-High Spatial Resolution Reconstruction of Fluorescence Molecular Tomography Based on 3-D Fusion Dual-Sampling Deep Neural Network

Fluorescence molecular tomography (FMT) is a promising and high sensitivity imaging modality that can reconstruct the three-dimensional (3D) distribution of interior fluorescent sources. However, the spatial resolution of FMT has encountered an insurmountable bottleneck and cannot be substantially improved, due to the simplified forward model and the severely ill-posed inverse problem. In this work, a 3D fusion dual-sampling convolutional neural network, namely UHR-DeepFMT, was proposed to achieve ultra-high spatial resolution reconstruction of FMT. Under this framework, the UHR-DeepFMT does not need to explicitly solve the FMT forward and inverse problems. Instead, it directly establishes an end-to-end mapping model to reconstruct the fluorescent sources, which can enormously eliminate the modeling errors. Besides, a novel fusion mechanism that integrates the dual-sampling strategy and the squeeze-and-excitation (SE) module is introduced into the skip connection of UHR-DeepFMT, which can significantly improve the spatial resolution by greatly alleviating the ill-posedness of the inverse problem. To evaluate the performance of UHR-DeepFMT network model, numerical simulations, physical phantom and in vivo experiments were conducted. The results demonstrated that the proposed UHR-DeepFMT can outperform the cutting-edge methods and achieve ultra-high spatial resolution reconstruction of FMT with the powerful ability to distinguish adjacent targets with a minimal edge-to-edge distance (EED) of 0.5 mm. It is assumed that this research is a significant improvement for FMT in terms of spatial resolution and overall imaging quality, which could promote the precise diagnosis and preclinical application of small animals in the future.

Deep learning: step forward to high-resolution in vivo shortwave infrared imaging

Shortwave infrared window (SWIR: 1000-1700 nm) represents a major improvement compared to the NIR-I region (700-900 nm) in terms of temporal and spatial resolutions in depths down to 4 mm. SWIR is a fast and cheap alternative to more precise methods such as X-ray and opto-acoustic imaging. Main obstacles in SWIR imaging are the noise and scattering from tissues and skin that reduce the precision of the method. We demonstrate that the combination of SWIR in vivo imaging in the NIR-IIb region (1500-1700 nm) with advanced deep learning image analysis allows to overcome these obstacles and making a large step forward to high resolution imaging: it allows to precisely segment vessels from tissues and noise, provides morphological structure of the vessels network, with learned pseudo-3D shape, their relative position, dynamic information of blood vascularization in depth in small animals and distinguish the vessels types: artieries and veins. For demonstration we use neural network IterNet that exploits structural redundancy of the blood vessels, which provides a useful analysis tool for raw SWIR images.

High-speed X-ray-induced luminescence computed tomography

X-ray-induced luminescence computed tomography (XLCT) is an emerging molecular imaging. Challenges in improving spatial resolution and reducing the scan time in a whole-body field of view (FOV) still remain for practical in vivo applications. In this study, we present a novel XLCT technique capable of obtaining three-dimensional (3D) images from a single snapshot. Specifically, a customed two-planar-mirror component is integrated into a cone beam XLCT imaging system to obtain multiple optical views of an object simultaneously. Furthermore, a compressive sensing based algorithm is adopted to improve the efficiency of 3D XLCT image reconstruction. Numerical simulations and experiments were conducted to validate the single snapshot X-ray-induced luminescence computed tomography (SS-XLCT). The results show that the 3D distribution of the nanophosphor targets can be visualized much faster than conventional cone beam XLCT imaging method that was used in our comparisons while maintaining comparable spatial resolution as in conventional XLCT imaging. SS-XLCT has the potential to harness the power of XLCT for rapid whole-body in vivo molecular imaging of small animals.

NIR-II/NIR-I Fluorescence Molecular Tomography of Heterogeneous Mice Based on Gaussian Weighted Neighborhood Fused Lasso Method

Fluorescence molecular tomography (FMT), which can visualize the distribution of fluorescence biomarkers, has become a novel three-dimensional noninvasive imaging technique for in vivo studies such as tumor detection and lymph node location. However, it remains a challenging problem to achieve satisfactory reconstruction performance of conventional FMT in the first near-infrared window (NIR-I, 700-900nm) because of the severe scattering of NIR-I light. In this study, a promising FMT method for heterogeneous mice was proposed to improve the reconstruction accuracy using the second near-infrared window (NIR-II, 1000-1700nm), where the light scattering significantly reduced compared with NIR-I. The optical properties of NIR-II were analyzed to construct the forward model for NIR-II FMT. Furthermore, to raise the accuracy of solution of the inverse problem, we proposed a novel Gaussian weighted neighborhood fused Lasso (GWNFL) method. Numerical simulation was performed to demonstrate the outperformance of GWNFL compared with other algorithms. Besides, a novel NIR-II/NIR-I dual-modality FMT system was developed to contrast the in vivo reconstruction performance between NIR-II FMT and NIR-I FMT. To compare the reconstruction performance of NIR-II FMT with traditional NIR-I FMT, numerical simulations and in vivo experiments were conducted. Both the simulation and in vivo results showed that NIR-II FMT outperformed NIR-I FMT in terms of location accuracy and spatial overlap index. It is believed that this study could promote the development and biomedical application of NIR-II FMT in the future.

How Non-invasive in vivo Cell Tracking Supports the Development and Translation of Cancer Immunotherapies

Immunotherapy is a relatively new treatment regimen for cancer, and it is based on the modulation of the immune system to battle cancer. Immunotherapies can be classified as either molecular or cell-based immunotherapies, and both types have demonstrated promising results in a growing number of cancers. Indeed, several immunotherapies representing both classes are already approved for clinical use in oncology. While spectacular treatment successes have been reported, particularly for so-called immune checkpoint inhibitors and certain cell-based immunotherapies, they have also been accompanied by a variety of severe, sometimes life-threatening side effects. Furthermore, not all patients respond to immunotherapy. Hence, there is the need for more research to render these promising therapeutics more efficacious, more widely applicable, and safer to use. Whole-body in vivo imaging technologies that can interrogate cancers and/or immunotherapies are highly beneficial tools for immunotherapy development and translation to the clinic. In this review, we explain how in vivo imaging can aid the development of molecular and cell-based anti-cancer immunotherapies. We describe the principles of imaging host T-cells and adoptively transferred therapeutic T-cells as well as the value of traceable cancer cell models in immunotherapy development. Our emphasis is on in vivo cell tracking methodology, including important aspects and caveats specific to immunotherapies. We discuss a variety of associated experimental design aspects including parameters such as cell type, observation times/intervals, and detection sensitivity. The focus is on non-invasive 3D cell tracking on the whole-body level including aspects relevant for both preclinical experimentation and clinical translatability of the underlying methodologies.

X-ray-induced shortwave infrared luminescence computed tomography

X-ray luminescence computed tomography (XLCT) based on x-ray-excitable nanophosphors has been proposed as a new modality for molecular imaging. The technique has two main advantages compared to other modalities. First, autofluorescence, which is problematic for fluorescence imaging, can be substantially reduced. Second, deep-tissue in vivo imaging with high optical contrast and spatial resolution becomes achievable. Here, we extend the novel XLCT modality from the visible or infrared region to a shortwave infrared wavelength by developing an x-ray-induced shortwave infrared luminescence computed tomography (SWIR-XLCT). For this application, rare-earth nanophosphors (RENPs) were synthesized as core/shell structures consisting of a Ho-doped NaYbF4 core surrounded by a NaYF4 shell that emit light efficiently in the shortwave infrared spectral region under x-ray excitation. Through numerical simulations and phantom experiments, we showed the feasibility of SWIR-XLCT and demonstrated its potential for x-ray luminescence imaging with high spatial resolution and deep depth.

An injectable hybrid hydrogel based on a genetically engineered polypeptide for second near-infrared fluorescence/photoacoustic imaging-monitored sustained chemo-photothermal therapy

An injectable multifunctional hydrogel based on an engineered coiled-coil polypeptide, Ag2S quantum dots (QDs), and paclitaxel (PTX) has been developed for sustained chemo-photothermal therapy. Oil-soluble Ag2S QDs and PTX were first loaded into nanogels formed with engineered polypeptide PC10A by ultrasonic treatment to prepare PC10A/Ag2S QD/PTX nanogels. The multifunctional PC10A/Ag2S QD/PTX hydrogels were prepared by dissolving the PC10A/Ag2S QD/PTX nanogels into the PC10A hydrogel. The PC10A/Ag2S QD/PTX hydrogel can be injected directly into the site of tumors. In vitro and in vivo toxicity results showed that the PC10A/Ag2S QD/PTX hydrogel presented excellent biocompatibility. Compared with single near-infrared photothermal therapy and chemotherapy, the combined therapy could effectively suppress the growth of SKOV3 ovarian carcinoma tumor. In addition, real-time monitoring of the in vivo degradation of the PC10A/Ag2S QD/PTX hydrogel was achieved by near-infrared fluorescence imaging and photoacoustic imaging. These results demonstrated that this injectable multifunctional PC10A/Ag2S QD/PTX hydrogel has the potential as a theranostic platform for sustained cancer treatments.

In situ aqueous synthesis of genetically engineered polypeptide-capped Ag2S quantum dots for second near-infrared fluorescence/photoacoustic imaging and photothermal therapy

Ag₂S quantum dots have received extensive attention as theranostic agents for second near-infrared (NIR-II) fluorescence and photoacoustic dual-mode imaging, and photothermal therapy. However, it is still greatly challenging to synthesize Ag₂S quantum dots using aqueous synthesis. In this study, genetically engineered polypeptide-capped Ag₂S quantum dots were successfully synthesized. Three cysteines were integrated to the C-terminal and N-terminal of RGDPC₁₀A to enhance the stability and brightness of the synthesized Ag₂S quantum dots. The RGDPC₁₀A-capped Ag₂S quantum dots exhibited excellent stability, outstanding resistance to photobleaching, and a superior quantum yield of up to 3.78% in the NIR-II biological window. The in vitro and in vivo results showed that the RGDPC₁₀A-capped Ag₂S quantum dots possessed typical NIR-II fluorescence, photoacoustic imaging, and photothermal therapeutic effectiveness against tumors. Moreover, the results of toxicity assays suggested that the RGDPC₁₀A-capped Ag₂S quantum dots have negligible long-term toxicity. These findings open up the possibility for synthesizing theranostic agents by using this aqueous method.

Multi-angle projection imaging of short-wave infrared (SWIR) fluorescence for small animal optical tomography

Short-wave infrared imaging in tissue in the 1000-2000 nm range is characterized by reduced photon scatter and comparable or higher absorption compared to the NIR-I regime. These characteristics have implications for the performance of fluorescence molecular tomography (FMT) techniques, potentially improving the resolution of sub-surface structure, possibly at the expense of depth sensitivity. To examine these questions, we have developed a SWIR small animal fluorescence tomography system. This instrument acquires multi-angle SWIR projection images of a stationary platform through a rotating gantry technique. These images are then processed for tomographic reconstruction of the SWIR fluorescence activity. Herein, we describe the development of this system and show multi-angle images from a mouse carcass containing a SWIR-specific fluorophore inclusion.

Recent methodology advances in fluorescence molecular tomography

Molecular imaging (MI) is a novel imaging discipline that has been continuously developed in recent years. It combines biochemistry, multimodal imaging, biomathematics, bioinformatics, cell & molecular physiology, biophysics, and pharmacology, and it provides a new technology platform for the early diagnosis and quantitative analysis of diseases, treatment monitoring and evaluation, and the development of comprehensive physiology. Fluorescence Molecular Tomography (FMT) is a type of optical imaging modality in MI that captures the three-dimensional distribution of fluorescence within a biological tissue generated by a specific molecule of fluorescent material within a biological tissue. Compared with other optical molecular imaging methods, FMT has the characteristics of high sensitivity, low cost, and safety and reliability. It has become the research frontier and research hotspot of optical molecular imaging technology. This paper took an overview of the recent methodology advances in FMT, mainly focused on the photon propagation model of FMT based on the radiative transfer equation (RTE), and the reconstruction problem solution consist of forward problem and inverse problem. We introduce the detailed technologies utilized in reconstruction of FMT. Finally, the challenges in FMT were discussed. This survey aims at summarizing current research hotspots in methodology of FMT, from which future research may benefit.

Deep learning-based mesoscopic fluorescence molecular tomography: an in silico study

Fluorescence molecular tomography (FMT), as well as mesoscopic FMT (MFMT) is widely employed to investigate molecular level processes ex vivo or in vivo. However, acquiring depth-localized and less blurry reconstruction still remains challenging, especially when fluorophore (dye) is located within large scattering coefficient media. Herein, a two-stage deep learning-based three-dimensional (3-D) reconstruction algorithm is proposed. The key point for the proposed algorithm is to employ a 3-D convolutional neural network to correctly predict the boundary of reconstructions, leading refined results. Compared with conventional algorithm, in silico experiments show that relative volume and absolute centroid error reduce over ∼ 50 % whereas intersection over union increases over 15% for most situations. These results preliminarily indicate the promising future of appropriately applying machine learning (deep learning)-based methods in MFMT.

Cousins at work: How combining medical with optical imaging enhances in vivo cell tracking

Microscopy and medical imaging are related in their exploitation of electromagnetic waves, but were developed to satisfy differing needs, namely to observe small objects or to look inside subjects/objects, respectively. Together, these techniques can help elucidate complex biological processes and better understand health and disease. A current major challenge is to delineate mechanisms governing cell migration and tissue invasion in organismal development, the immune system and in human diseases such as cancer where the spatiotemporal tracking of small cell numbers in live animal models is extremely challenging. Multi-modal multi-scale in vivo cell tracking integrates medical and optical imaging. Fuelled by basic research in cancer biology and cell-based therapeutics, it has been enabled by technological advances providing enhanced resolution, sensitivity and multiplexing capabilities. Here, we review which imaging modalities have been successfully used for in vivo cell tracking and how this challenging task has benefitted from combining macroscopic with microscopic techniques.

A multifunctional targeting probe with dual-mode imaging and photothermal therapy used in vivo

Background

Ag₂S has the characteristics of conventional quantum dot such as broad excitation spectrum, narrow emission spectrum, long fluorescence lifetime, strong anti-bleaching ability, and other optical properties. Moreover, since its fluorescence emission is located in the NIR-II region, has stronger penetrating ability for tissue. Ag₂S quantum dot has strong absorption during the visible and NIR regions, it has good photothermal and photoacoustic response under certain wavelength excitation.

Results

200 nm aqueous probe Ag₂S@DSPE-PEG₂₀₀₀-FA (Ag₂S@DP-FA) with good dispersibility and stability was prepared by coating hydrophobic Ag₂S with the mixture of folic acid (FA) modified DSPE-PEG₂₀₀₀ (DP) and other polymers, it was found the probe had good fluorescent, photoacoustic and photothermal responses, and a low cell cytotoxicity at 50 μg/mL Ag concentration. Blood biochemical analysis, liver enzyme and tissue histopathological test showed that no significant influence was observed on blood and organs within 15 days after injection of the probe. In vivo and in vitro fluorescence and photoacoustic imaging of the probe further demonstrated that the Ag₂S@DP-FA probe had good active targeting ability for tumor. In vivo and in vitro photothermal therapy experiments confirmed that the probe also had good ability of killing tumor by photothermal.

Conclusions

Ag₂S@DP-FA was a safe, integrated diagnosis and treatment probe with multi-mode imaging, photothermal therapy and active targeting ability, which had a great application prospect in the early diagnosis and treatment of tumor.

Electronic supplementary material

The online version of this article (10.1186/s12951-018-0367-9) contains supplementary material, which is available to authorized users.

3D NIR-II Molecular Imaging Distinguishes Targeted Organs with High-Performance NIR-II Bioconjugates

Greatly reduced scattering in the second near-infrared (NIR-II) region (1000-1700 nm) opens up many new exciting avenues of bioimaging research, yet NIR-II fluorescence imaging is mostly implemented by using nontargeted fluorophores or wide-field imaging setups, limiting the signal-to-background ratio and imaging penetration depth due to poor specific binding and out-of-focus signals. A newly developed high-performance NIR-II bioconjugate enables targeted imaging of a specific organ in the living body with high quality. Combined with a home-built NIR-II confocal set-up, the enhanced imaging technique allows 900 µm-deep 3D organ imaging without tissue clearing techniques. Bioconjugation of two hormones to nonoverlapping NIR-II fluorophores facilitates two-color imaging of different receptors, demonstrating unprecedented multicolor live molecular imaging across the NIR-II window. This deep tissue imaging of specific receptors in live animals allows development of noninvasive molecular imaging of multifarious models of normal and neoplastic organs in vivo, beyond the traditional visible to NIR-I range. The developed NIR-II fluorescence microscopy will become a powerful imaging technique for deep tissue imaging without any physical sectioning or clearing treatment of the tissue.

Synchronization-based clustering algorithm for reconstruction of multiple reconstructed targets in fluorescence molecular tomography

Fluorescence molecular tomography (FMT) is an important in vivo molecular imaging technique and has been widely studied in preclinical research. Many methods perform well in the reconstruction of a single fluorescent target but may fail in reconstructing multiple targets because of the severe ill-posedness of the FMT inverse problem. In this paper the original synchronization-inspired clustering algorithm (OSC) is introduced into FMT for resolving multiple targets from the reconstruction result. Based on OSC, a synchronization-based clustering algorithm for FMT (SC-FMT) is developed to further improve location accuracy. Both algorithms utilize the minimum spanning tree to automatically identify the number of the reconstructed targets without prior information and human intervention. A serial of numerical simulation results demonstrates that SC-FMT and OSC can resolve multiple targets robustly and automatically, which also shows the potential of the proposed postprocessing algorithms in FMT reconstruction.

Graphene oxide coating core–shell silver sulfide@mesoporous silica for active targeted dual-mode imaging and chemo-photothermal synergistic therapy against tumors

FA-modified-GO coated core–shell Ag₂S@mesoporous silica where DOX was loaded for active targeting FL/PA imaging and synergistic chemo-photothermal therapy of tumors.

Molecular Imaging of Acute Cardiac Transplant Rejection: Animal Experiments and Prospects

Acute rejection (AR) remains the biggest challenge during the first year after heart transplantation despite advances in immunosuppressive therapy. The early detection and curbing of AR are crucial to the survival of transplant recipients. However, as the criterion standard for AR, endomyocardial biopsy has several limitations because of its inherent invasiveness and morbidity. Traditional imaging techniques, such as echocardiography and cardiac magnetic resonance imaging, are of certain value for AR, but their diagnostic criteria and accuracy remain in question. Molecular imaging sheds new light on AR diagnosis because it can provide information about gene expression and the location of molecules and cells. This article reviews the latest research and applications of several typical modalities of molecular imaging used in AR and discusses their advantages and disadvantages.

Fast and Robust Reconstruction for Fluorescence Molecular Tomography via L1-2 Regularization

Sparse reconstruction inspired by compressed sensing has attracted considerable attention in fluorescence molecular tomography (FMT). However, the columns of system matrix used for FMT reconstruction tend to be highly coherent, which means L₁ minimization may not produce the sparsest solution. In this paper, we propose a novel reconstruction method by minimization of the difference of L₁ and L₂ norms. To solve the nonconvex L_1-2 minimization problem, an iterative method based on the difference of convex algorithm (DCA) is presented. In each DCA iteration, the update of solution involves an L₁ minimization subproblem, which is solved by the alternating direction method of multipliers with an adaptive penalty. We investigated the performance of the proposed method with both simulated data and in vivo experimental data. The results demonstrate that the DCA for L_1-2 minimization outperforms the representative algorithms for L₁, L₂, L_1/2, and L₀ when the system matrix is highly coherent.