Small animal fluorescence and bioluminescence tomography: a review of approaches, algorithms and technology update

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.

Highly robust reconstruction framework for three-dimensional optical imaging based on physical model constrained neural networks

Objective. The reconstruction of three-dimensional optical imaging that can quantitatively acquire the target distribution from surface measurements is a serious ill-posed problem. Traditional regularization-based reconstruction can solve such ill-posed problem to a certain extent, but its accuracy is highly dependent ona priorinformation, resulting in a less stable and adaptable method. Data-driven deep learning-based reconstruction avoids the errors of light propagation models and the reliance on experience and a prior by learning the mapping relationship between the surface light distribution and the target directly from the dataset. However, the acquisition of the training dataset and the training of the network itself are time consuming, and the high dependence of the network performance on the training dataset results in a low generalization ability. The objective of this work is to develop a highly robust reconstruction framework to solve the existing problems.Approach. This paper proposes a physical model constrained neural networks-based reconstruction framework. In the framework, the neural networks are to generate a target distribution from surface measurements, while the physical model is used to calculate the surface light distribution based on this target distribution. The mean square error between the calculated surface light distribution and the surface measurements is then used as a loss function to optimize the neural network. To further reduce the dependence ona prioriinformation, a movable region is randomly selected and then traverses the entire solution interval. We reconstruct the target distribution in this movable region and the results are used as the basis for its next movement.Main Results. The performance of the proposed framework is evaluated with a series of simulations andin vivoexperiment, including accuracy robustness of different target distributions, noise immunity, depth robustness, and spatial resolution. The results collectively demonstrate that the framework can reconstruct targets with a high accuracy, stability and versatility.Significance. The proposed framework has high accuracy and robustness, as well as good generalizability. Compared with traditional regularization-based reconstruction methods, it eliminates the need to manually delineate feasible regions and adjust regularization parameters. Compared with emerging deep learning assisted methods, it does not require any training dataset, thus saving a lot of time and resources and solving the problem of poor generalization and robustness of deep learning methods. Thus, the framework opens up a new perspective for the reconstruction of three-dimension optical imaging.

FSMN-Net: a free space matching network based on manifold convolution for optical molecular tomography

Optical molecular tomography (OMT) can monitor glioblastomas in small animals non-invasively. Although deep learning (DL) methods have made remarkable achievements in this field, improving its generalization against diverse reconstruction systems remains a formidable challenge. In this Letter, a free space matching network (FSMN-Net) was presented to overcome the parameter mismatch problem in different reconstruction systems. Specifically, a novel, to the best of our knowledge, manifold convolution operator was designed by considering the mathematical model of OMT as a space matching process. Based on the dynamic domain expansion concept, an end-to-end fully convolutional codec further integrates this operator to realize robust reconstruction with voxel-level accuracy. The results of numerical simulations and in vivo experiments demonstrate that the FSMN-Net can stably generate high-resolution reconstruction volumetric images under different reconstruction systems.

Development of an integrated dual-modality 3D bioluminescence tomography and ultrasound imaging system for small animal tumor imaging

Ultrasound (US) is a valuable tool for imaging soft tissue and visualizing tumor contours. Taking the benefits of US, we presented an integrated dual-modality imaging system in this paper that achieves three-dimensional (3D) bioluminescence tomography (BLT) with multi-view bioluminescence images and 3D US imaging. The purpose of this system is to perform non-invasive, long-term monitoring of tumor growth in 3D images. US images can enhance the accuracy of the 3D BLT reconstruction and the bioluminescence dose within an object. Furthermore, an integrated co-registered scanning geometry was used to capture the fused BLT and US images. We validated the system with an in vivo experiment involving tumor-bearing mice. The results demonstrated the feasibility of reconstructing 3D BLT images in the tumor region using 3D US images. We used the dice coefficient and locational error to evaluate the similarity between the reconstructed source region and the actual source region. The dice coefficient was 88.5%, and the locational error was 0.4 mm when comparing the BLT and 3D US images. The hybrid BLT/US system could provide significant benefits for reconstructing the source of tumor location and conducting quantitative analysis of tumor size.

Noninvasive Visualization of Amyloid-Beta Deposits in Alzheimer's Amyloidosis Mice via Fluorescence Molecular Tomography Using Contrast Agent

Alzheimer's disease is pathologically featured by the accumulation of amyloid-beta (Aβ) plaque and neurofibrillary tangles. Compared to small animal positron emission tomography, optical imaging features nonionizing radiation, low cost, and logistic convenience. Optical detection of Aβ deposits is typically implemented by 2D macroscopic imaging and various microscopic techniques assisted with Aβ-targeted contrast agents. Here, we introduce fluorescence molecular tomography (FMT), a macroscopic 3D fluorescence imaging technique, convenient for in vivo longitudinal monitoring of the animal brain without the involvement of cranial window opening operation. This chapter aims to provide the protocols for FMT in vivo imaging of Aβ deposits in the brain of rodent model of Alzheimer's disease. The materials, stepwise method, notes, limitations of FMT, and emerging opportunities for FMT techniques are presented.

Regularized reconstruction based on joint smoothly clipped absolute deviation regularization and graph manifold learning for fluorescence molecular tomography

Objective.Fluorescence molecular tomography (FMT) is an optical imaging modality that provides high sensitivity and low cost, which can offer the three-dimensional distribution of biomarkers by detecting the fluorescently labeled probe noninvasively. In the field of preclinical cancer diagnosis and treatment, FMT has gained significant traction. Nonetheless, the current FMT reconstruction results suffer from unsatisfactory morphology and location accuracy of the fluorescence distribution, primarily due to the light scattering effect and the ill-posed nature of the inverse problem.Approach.To address these challenges, a regularized reconstruction method based on joint smoothly clipped absolute deviation regularization and graph manifold learning (SCAD-GML) for FMT is presented in this paper. The SCAD-GML approach combines the sparsity of the fluorescent sources with the latent manifold structure of fluorescent source distribution to achieve more accurate and sparse reconstruction results. To obtain the reconstruction results efficiently, the non-convex gradient descent iterative method is employed to solve the established objective function. To assess the performance of the proposed SCAD-GML method, a comprehensive evaluation is conducted through numerical simulation experiments as well asin vivoexperiments.Main results.The results demonstrate that the SCAD-GML method outperforms other methods in terms of both location and shape recovery of fluorescence biomarkers distribution.Siginificance.These findings indicate that the SCAD-GML method has the potential to advance the application of FMT inin vivobiological research.

3D-deep optical learning: a multimodal and multitask reconstruction framework for optical molecular tomography

Optical molecular tomography (OMT) is an emerging imaging technique. To date, the poor universality of reconstruction algorithms based on deep learning for various imaged objects and optical probes limits the development and application of OMT. In this study, based on a new mapping representation, a multimodal and multitask reconstruction framework-3D deep optical learning (3DOL), was presented to overcome the limitations of OMT in universality by decomposing it into two tasks, optical field recovery and luminous source reconstruction. Specifically, slices of the original anatomy (provided by computed tomography) and boundary optical measurement of imaged objects serve as inputs of a recurrent convolutional neural network encoded parallel to extract multimodal features, and 2D information from a few axial planes within the samples is explicitly incorporated, which enables 3DOL to recognize different imaged objects. Subsequently, the optical field is recovered under the constraint of the object geometry, and then the luminous source is segmented by a learnable Laplace operator from the recovered optical field, which obtains stable and high-quality reconstruction results with extremely few parameters. This strategy enable 3DOL to better understand the relationship between the boundary optical measurement, optical field, and luminous source to improve 3DOL's ability to work in a wide range of spectra. The results of numerical simulations, physical phantoms, and in vivo experiments demonstrate that 3DOL is a compatible deep-learning approach to tomographic imaging diverse objects. Moreover, the fully trained 3DOL under specific wavelengths can be generalized to other spectra in the 620-900 nm NIR-I window.

Structure-fused deep 3D hierarchical network: A bioluminescence tomography scheme for different imaging objects

Monte Carlo eXtreme (MCX) method has a unique advantage for deep neural network based bioluminescence tomography (BLT) reconstruction. However, this method ignores the distribution of sources energy and relies on the determined tissue structure. In this paper, a deep 3D hierarchical reconstruction network for BLT was proposed where the inputs were divided into two parts -- bioluminescence image (BLI) and anatomy of the imaged object by CT. Firstly, a parallel encoder is used to feature the original BLI & CT slices and integrate their features to distinguish the different tissue structure of imaging objects; Secondly, GRU is used to fit the spatial information of different slices and convert it into 3D features; Finally, the 3D features are decoded to the spacial and energy information of source by a symmetrical decoding structure. Our research suggested that this method can effectively compute the radiation intensity and the spatial distribution of the source for different imaging object.

Regularization parameter based on incomplete variables for X-ray luminescence computed tomography

X-ray luminescence computed tomography (XLCT) is an emerging molecular imaging technique for biological application. However, it is still a challenge to get a stable and accurate solution of the reconstruction of XLCT. This paper presents a regularization parameter selection strategy based on incomplete variables frame for XLCT. A residual information, which is derived from Karush-Kuhn-Tucker (KKT) equivalent condition, is employed to determine the regularization parameter. This residual contains the relevant information about the solution norm and gradient norm, which improved the recovered results. Simulation and phantom experiments are designed to test the performance of the algorithm.Clinical Relevance- The results have not yet been used in clinical relevance currently, we believed that this strategy will facilitate the development of the preclinical applications in FMT.

Reconstruction of fluorophore absorption and fluorescence lifetime using early photon mesoscopic fluorescence molecular tomography: a phantom study

SIGNIFICANCE

Fluorescence molecular lifetime tomography (FMLT) plays an increasingly important role in experimental oncology. The article presents and experimentally verifies an original method of mesoscopic time domain FMLT, based on an asymptotic approximation to the fluorescence source function, which is valid for early arriving photons.

AIM

The aim was to justify the efficiency of the method by experimental scanning and reconstruction of a phantom with a fluorophore. The experimental facility included the TCSPC system, the pulsed supercontinuum Fianium laser, and a three-channel fiber probe. Phantom scanning was done in mesoscopic regime for three-dimensional (3D) reflectance geometry.

APPROACH

The sensitivity functions were simulated with a Monte Carlo method. A compressed-sensing-like reconstruction algorithm was used to solve the inverse problem for the fluorescence parameter distribution function, which included the fluorophore absorption coefficient and fluorescence lifetime distributions. The distributions were separated directly in the time domain with the QR-factorization least square method.

RESULTS

3D tomograms of fluorescence parameters were obtained and analyzed using two strategies for the formation of measurement data arrays and sensitivity matrices. An algorithm is developed for the flexible choice of optimal strategy in view of attaining better reconstruction quality. Variants on how to improve the method are proposed, specifically, through stepped extraction and further use of a posteriori information about the object.

CONCLUSIONS

Even if measurement data are limited, the proposed method is capable of giving adequate reconstructions but their quality depends on available a priori (or a posteriori) information. Further research aims to improve the method by implementing the variants proposed.

Development of a theranostic preclinical fluorescence molecular tomography/cone beam CT-guided irradiator platform

Image-guided small animal radiation research platforms allow more precise radiation treatment. Commercially available small animal X-ray irradiators are often equipped with a CT/cone-beam CT (CBCT) component for target guidance. Besides having poor soft-tissue contrast, CBCT unfortunately cannot provide molecular information due to its low sensitivity. Hence, there are extensive efforts to incorporate a molecular imaging component besides CBCT on these radiation therapy platforms. As an extension of these efforts, here we present a theranostic fluorescence tomography/CBCT-guided irradiator platform that provides both anatomical and molecular guidance, which can overcome the limitations of stand-alone CBCT. The performance of our hybrid system is validated using both tissue-like phantoms and mice ex vivo. Both studies show that fluorescence tomography can provide much more accurate quantitative results when CBCT-derived structural information is used to constrain the inverse problem. The error in the recovered fluorescence absorbance reduces nearly 10-fold for all cases, from approximately 60% down to 6%. This is very significant since high quantitative accuracy in molecular information is crucial to the correct assessment of the changes in tumor microenvironment related to radiation therapy.

ABPO-TVSCAD: alternating Bregman proximity operators approach based on TVSCAD regularization for bioluminescence tomography

Objective.Bioluminescence tomography (BLT) is a promising non-invasive optical medical imaging technique, which can visualize and quantitatively analyze the distribution of tumor cells in living tissues. However, due to the influence of photon scattering effect and ill-conditioned inverse problem, the reconstruction result is unsatisfactory. The purpose of this study is to improve the reconstruction performance of BLT.Approach.An alternating Bregman proximity operators (ABPO) method based on TVSCAD regularization is proposed for BLT reconstruction. TVSCAD combines the anisotropic total variation (TV) regularization constraints and the non-convex smoothly clipped absolute deviation (SCAD) penalty constraints, to make a trade-off between the sparsity and edge preservation of the source. ABPO approach is used to solve the TVSCAD model (ABPO-TVSCAD for short). In addition, to accelerate the convergence speed of the ABPO, we adapt the strategy of shrinking the permission source region, which further improves the performance of ABPO-TVSCAD.Main results.The results of numerical simulations andin vivoxenograft mouse experiment show that our proposed method achieved superior accuracy in spatial localization and morphological reconstruction of bioluminescent source.Significance.ABPO-TVSCAD is an effective and robust reconstruction method for BLT, and we hope that this method can promote the development of optical molecular tomography.

Monitoring autochthonous lung tumors induced by somatic CRISPR gene editing in mice using a secreted luciferase

Background

In vivo gene editing of somatic cells with CRISPR nucleases has facilitated the generation of autochthonous mouse tumors, which are initiated by genetic alterations relevant to the human disease and progress along a natural timeline as in patients. However, the long and variable, orthotopic tumor growth in inner organs requires sophisticated, time-consuming and resource-intensive imaging for longitudinal disease monitoring and impedes the use of autochthonous tumor models for preclinical studies.

Methods

To facilitate a more widespread use, we have generated a reporter mouse that expresses a Cre-inducible luciferase from Gaussia princeps (GLuc), which is secreted by cells in an energy-consuming process and can be measured quantitatively in the blood as a marker for the viable tumor load. In addition, we have developed a flexible, complementary toolkit to rapidly assemble recombinant adenoviruses (AVs) for delivering Cre recombinase together with CRISPR nucleases targeting cancer driver genes.

Results

We demonstrate that intratracheal infection of GLuc reporter mice with CRISPR-AVs efficiently induces lung tumors driven by mutations in the targeted cancer genes and simultaneously activates the GLuc transgene, resulting in GLuc secretion into the blood by the growing tumor. GLuc blood levels are easily and robustly quantified in small-volume blood samples with inexpensive equipment, enable tumor detection already several months before the humane study endpoint and precisely mirror the kinetics of tumor development specified by the inducing gene combination.

Conclusions

Our study establishes blood-based GLuc monitoring as an inexpensive, rapid, high-throughput and animal-friendly method to longitudinally monitor autochthonous tumor growth in preclinical studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12943-022-01661-2.

Three-Dimensional Deep-Tissue Functional and Molecular Imaging by Integrated Photoacoustic, Ultrasound, and Angiographic Tomography (PAUSAT)

Non-invasive small-animal imaging technologies, such as optical imaging, magnetic resonance imaging and x -ray computed tomography, have enabled researchers to study normal biological phenomena or disease progression in their native conditions. However, existing small-animal imaging technologies often lack either the penetration capability for interrogating deep tissues (e.g., optical microscopy), or the functional and molecular sensitivity for tracking specific activities (e.g., magnetic resonance imaging). To achieve functional and molecular imaging in deep tissues, we have developed an integrated photoacoustic, ultrasound and acoustic angiographic tomography (PAUSAT) system by seamlessly combining light and ultrasound. PAUSAT can perform three imaging modes simultaneously with complementary contrast: high-frequency B-mode ultrasound imaging of tissue morphology, microbubble-enabled acoustic angiography of tissue vasculature, and multi-spectral photoacoustic imaging of molecular probes. PAUSAT can provide three-dimensional (3D) multi-contrast images that are co-registered, with high spatial resolutions at large depths. Using PAUSAT, we performed proof-of-concept in vivo experiments on various small animal models: monitoring longitudinal development of placenta and embryo during mouse pregnancy, tracking biodistribution and metabolism of near-infrared organic dye on the whole-body scale, and detecting breast tumor expressing genetically-encoded photoswitchable phytochromes. These results have collectively demonstrated that PAUSAT has broad applicability in biomedical research, providing comprehensive structural, functional, and molecular imaging of small animal models.

Self-Training Strategy Based on Finite Element Method for Adaptive Bioluminescence Tomography Reconstruction

Bioluminescence tomography (BLT) is a promising pre-clinical imaging technique for a wide variety of biomedical applications, which can non-invasively reveal functional activities inside living animal bodies through the detection of visible or near-infrared light produced by bioluminescent reactions. Recently, reconstruction approaches based on deep learning have shown great potential in optical tomography modalities. However, these reports only generate data with stationary patterns of constant target number, shape, and size. The neural networks trained by these data sets are difficult to reconstruct the patterns outside the data sets. This will tremendously restrict the applications of deep learning in optical tomography reconstruction. To address this problem, a self-training strategy is proposed for BLT reconstruction in this paper. The proposed strategy can fast generate large-scale BLT data sets with random target numbers, shapes, and sizes through an algorithm named random seed growth algorithm and the neural network is automatically self-trained. In addition, the proposed strategy uses the neural network to build a map between photon densities on surface and inside the imaged object rather than an end-to-end neural network that directly infers the distribution of sources from the photon density on surface. The map of photon density is further converted into the distribution of sources through the multiplication with stiffness matrix. Simulation, phantom, and mouse studies are carried out. Results show the availability of the proposed self-training strategy.

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 deep learning and Monte Carlo based framework for bioluminescence imaging center of mass-guided glioblastoma targeting

Objective. Bioluminescence imaging (BLI) is a valuable tool for non-invasive monitoring of glioblastoma multiforme (GBM) tumor-bearing small animals without incurring x-ray radiation burden. However, the use of this imaging modality is limited due to photon scattering and lack of spatial information. Attempts at reconstructing bioluminescence tomography (BLT) using mathematical models of light propagation show limited progress. Approach. This paper employed a different approach by using a deep convolutional neural network (CNN) to predict the tumor’s center of mass (CoM). Transfer-learning with a sizeable artificial database is employed to facilitate the training process for, the much smaller, target database including Monte Carlo (MC) simulations of real orthotopic glioblastoma models. Predicted CoM was then used to estimate a BLI-based planning target volume (bPTV), by using the CoM as the center of a sphere, encompassing the tumor. The volume of the encompassing target sphere was estimated based on the total number of photons reaching the skin surface. Main results. Results show sub-millimeter accuracy for CoM prediction with a median error of 0.59 mm. The proposed method also provides promising performance for BLI-based tumor targeting with on average 94% of the tumor inside the bPTV while keeping the average healthy tissue coverage below 10%. Significance. This work introduced a framework for developing and using a CNN for targeted radiation studies for GBM based on BLI. The framework will enable biologists to use BLI as their main image-guidance tool to target GBM tumors in rat models, avoiding delivery of high x-ray imaging dose to the animals.

Non-invasive visualization of amyloid-beta deposits in Alzheimer amyloidosis mice using magnetic resonance imaging and fluorescence molecular tomography

Abnormal cerebral accumulation of amyloid-beta peptide (Aβ) is a major hallmark of Alzheimer's disease. Non-invasive monitoring of Aβ deposits enables assessing the disease burden in patients and animal models mimicking aspects of the human disease as well as evaluating the efficacy of Aβ-modulating therapies. Previous in vivo assessments of plaque load have been predominantly based on macroscopic fluorescence reflectance imaging (FRI) and confocal or two-photon microscopy using Aβ-specific imaging agents. However, the former method lacks depth resolution, whereas the latter is restricted by the limited field of view preventing a full coverage of the large brain region. Here, we utilized a fluorescence molecular tomography (FMT)-magnetic resonance imaging (MRI) pipeline with the curcumin derivative fluorescent probe CRANAD-2 to achieve full 3D brain coverage for detecting Aβ accumulation in the arcAβ mouse model of cerebral amyloidosis. A homebuilt FMT system was used for data acquisition, whereas a customized software platform enabled the integration of MRI-derived anatomical information as prior information for FMT image reconstruction. The results obtained from the FMT-MRI study were compared to those from conventional planar FRI recorded under similar physiological conditions, yielding comparable time courses of the fluorescence intensity following intravenous injection of CRANAD-2 in a region-of-interest comprising the brain. In conclusion, we have demonstrated the feasibility of visualizing Aβ deposition in 3D using a multimodal FMT-MRI strategy. This hybrid imaging method provides complementary anatomical, physiological and molecular information, thereby enabling the detailed characterization of the disease status in arcAβ mouse models, which can also facilitate monitoring the efficacy of putative treatments targeting Aβ.

Superpixel spectral unmixing framework for the volumetric assessment of tissue chromophores: A photoacoustic data-driven approach

The assessment of tissue chromophores at a volumetric scale is vital for an improved diagnosis and treatment of a large number of diseases. Spectral photoacoustic imaging (sPAI) co-registered with high-resolution ultrasound (US) is an innovative technology that has a great potential for clinical translation as it can assess the volumetric distribution of the tissue components. Conventionally, to detect and separate the chromophores from sPAI, an input of the expected tissue absorption spectra is required. However, in pathological conditions, the prediction of the absorption spectra is difficult as it can change with respect to the physiological state. Besides, this conventional approach can also be hampered due to spectral coloring, which is a prominent distortion effect that induces spectral changes at depth. Here, we are proposing a novel data-driven framework that can overcome all these limitations and provide an improved assessment of the tissue chromophores. We have developed a superpixel spectral unmixing (SPAX) approach that can detect the most and less prominent absorber spectra and their volumetric distribution without any user interactions. Within the SPAX framework, we have also implemented an advanced spectral coloring compensation approach by utilizing US image segmentation and Monte Carlo simulations, based on a predefined library of optical properties. The framework has been tested on tissue-mimicking phantoms and also on healthy animals. The obtained results show enhanced specificity and sensitivity for the detection of tissue chromophores. To our knowledge, this is a unique framework that accounts for the spectral coloring and provides automated detection of tissue spectral signatures at a volumetric scale, which can open many possibilities for translational research.

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.

Regulatory guidelines and preclinical tools to study the biodistribution of RNA therapeutics

The success of the messenger RNA-based COVID-19 vaccines of Moderna and Pfizer/BioNTech marks the beginning of a new chapter in modern medicine. However, the rapid rise of mRNA therapeutics has resulted in a regulatory framework that is somewhat lagging. The current guidelines either do not apply, do not mention RNA therapeutics, or do not have widely accepted definitions. This review describes the guidelines for preclinical biodistribution studies of mRNA/siRNA therapeutics and highlights the relevant differences for mRNA vaccines. We also discuss the role of in vivo RNA imaging techniques and other assays to fulfill and/or complement the regulatory requirements. Specifically, quantitative whole-body autoradiography, microautoradiography, mass spectrometry-based assays, hybridization techniques (FISH, bDNA), PCR-based methods, in vivo fluorescence imaging, and in vivo bioluminescence imaging, are discussed. We conclude that this new and rapidly evolving class of medicines demands a multi-layered approach to fully understand its biodistribution and in vivo characteristics.

Method for improving the accuracy of fluorescence molecular tomography based on multi-wavelength concurrent reconstruction

A Concurrent-wavelength Reconstruction Algorithm (CRA) based on multi-wavelength information fusion is proposed in this paper that aims to further improve the accuracy of Fluorescence Molecular Tomography (FMT) reconstruction. Combining multi-spectral data with FMT technology, the information of 650 and 750 nm wavelengths near-infrared was used to increase the feature information of the dominant 850 nm wavelength near-infrared effectively. Principal component analysis, which can remove redundant information and achieve data dimensionality reduction, was then utilized to extract the feature information. Finally, tomographic reconstruction of the anomalies was performed based on the stacked auto-encoder neural network model. The comparison results of numerical experiments showed that the reconstruction effect of CRA was superior to the performance of the single wavelength model. The correlation coefficient between CRA reconstructed anomalies' fluorescence yield values and the real fluorescence yield values remained at 0.95 or more under the noise of different levels of signal-to-noise ratios. Therefore, the CRA proposed in this paper could effectively improve on the ill-posedness of the inverse problem, which could further enhance the accuracy of FMT reconstruction.

Bioluminescence tomography reconstruction in conjunction with an organ probability map as an anatomical reference

To alleviate the ill-posedness of bioluminescence tomography (BLT) reconstruction, anatomical information from computed tomography (CT) or magnetic resonance imaging (MRI) is usually adopted to improve the reconstruction quality. With the anatomical information, different organs could be segmented and assigned with appropriate optical parameters, and the reconstruction could be confined into certain organs. However, image segmentation is a time-consuming and challenging work, especially for the low-contrast organs. In this paper, we present a BLT reconstruction method in conjunction with an organ probability map to effectively incorporate the anatomical information. Instead of using a segmentation with a fixed organ map, an organ probability map is established by registering the CT image of the mouse to the statistical mouse atlas with the constraints of the mouse surface and high-contrast organs (bone and lung). Then the organ probability map of the low-contrast organs, such as the liver and kidney, is determined automatically. After discretization of the mouse torso, a heterogeneous model is established as the input for reconstruction, in which the optical parameter of each node is calculated according to the organ probability map. To take the advantage of the sparse Bayesian Learning (SBL) method in recovering block sparse signals in inverse problems, which is common in BLT applications where the target distribution has the characteristic of sparsity and block structure, a two-step method in conjunction with the organ probability map is presented. In the first step, a fast sparse algorithm, L1-LS, is used to reveal the source distribution on the organ level. In the second step, the bioluminescent source is reconstructed on the pixel level based on the SBL method. Both simulation and in vivo experiments are conducted, and the results demonstrate that the organ probability map in conjunction with the proposed two-step BLT reconstruction method is feasible to accurately reconstruct the localization of the bioluminescent light source.

OPK_SNCA: Optimized prior knowledge via sparse non-convex approach for cone-beam X-ray luminescence computed tomography imaging

BACKGROUND

The development of Cone-beam X-ray luminescence computed tomography (CB-XLCT) has allowed the quantitative in-depth biological imaging, but with a greatly ill-posed and ill-conditioned inverse problem. Although the predefined permissible source region (PSR) is a widely used way to alleviate the problem for CB-XLCT imaging, how to obtain the accurate PSR is still a challenge for the process of inverse reconstruction.

METHODS

We proposed an optimized prior knowledge via a sparse non-convex approach (OPK_SNCA) for CB-XLCT imaging. Firstly, non-convex L_p-norm optimization model was employed for copying with the inverse problem, and an iteratively reweighted split augmented lagrangian shrinkage algorithm was developed to obtain a group of sparse solutions based on different non-convex p values. Secondly, a series of permissible regions (PRs) with different discretized mesh was further achieved, and the intersection operation was implemented on the group of PRs to get a reasonable PSR. After that, the final PSR was adopted as an optimized prior knowledge to enhance the reconstruction quality of inverse reconstruction.

RESULTS

Both simulation experiments and in vivo experiment were performed to evaluate the efficiency and robustness of the proposed method.

CONCLUSIONS

The experimental results demonstrated that our proposed method could significantly improve the imaging quality of the distribution of X-ray-excitable nanophosphors for CB-XLCT.

Localization of plane waves in the stochastic telegrapher's equation

From the exact solution of the stochastic telegrapher's equation, Fourier plane-wave-like modes are introduced. Then the time evolution of the plane-wave modes are analyzed when the absorption of energy in the telegrapher's equation has strong time fluctuations. We demonstrate that fluctuations in the loss of energy introduce a localized gap with a size that depends on the correlation timescale of the fluctuations. We prove that for a large time correlation the gap is strongly reduced, which means that there is delocalization in the plane-wave modes with respect to the plane waves in the ordinary telegrapher's equation. This result is of relevance in the study of the transport of electromagnetic waves in a conducting medium, and sheds light on the functional role of the fluctuations in the loss of energy in the telegrapher's dynamics.

Imaging in experimental models of diabetes

Translational medicine, experimental medicine and experimental animal models, in particular mice and rats, represent a multidisciplinary field that has made it possible to achieve, in the last decades, important scientific progress. In this review, we have summarized the most frequently used imaging animal models, such as ultrasound (US), micro-CT, MRI and the optical imaging methods, and their main implications in diagnostic and therapeutic fields, with a particular focus on diabetes mellitus, a multifactorial disease extremely widespread among the general population.

Compressed sensing in fluorescence microscopy

Compressed sensing (CS) is a signal processing approach that solves ill-posed inverse problems, from under-sampled data with respect to the Nyquist criterium. CS exploits sparsity constraints based on the knowledge of prior information, relative to the structure of the object in the spatial or other domains. It is commonly used in image and video compression as well as in scientific and medical applications, including computed tomography and magnetic resonance imaging. In the field of fluorescence microscopy, it has been demonstrated to be valuable for fast and high-resolution imaging, from single-molecule localization, super-resolution to light-sheet microscopy. Furthermore, CS has found remarkable applications in the field of mesoscopic imaging, facilitating the study of small animals' organs and entire organisms. This review article illustrates the working principles of CS, its implementations in optical imaging and discusses several relevant uses of CS in the field of fluorescence imaging from super-resolution microscopy to mesoscopy.

Macroscopic Fluorescence Lifetime Imaging for Monitoring of Drug-Target Engagement

Precision medicine promises to improve therapeutic efficacy while reducing adverse effects, especially in oncology. However, despite great progresses in recent years, precision medicine for cancer treatment is not always part of routine care. Indeed, the ability to specifically tailor therapies to distinct patient profiles requires still significant improvements in targeted therapy development as well as decreases in drug treatment failures. In this regard, preclinical animal research is fundamental to advance our understanding of tumor biology, and diagnostic and therapeutic response. Most importantly, the ability to measure drug-target engagement accurately in live and intact animals is critical in guiding the development and optimization of targeted therapy. However, a major limitation of preclinical molecular imaging modalities is their lack of capability to directly and quantitatively discriminate between drug accumulation and drug-target engagement at the pathological site. Recently, we have developed Macroscopic Fluorescence Lifetime Imaging (MFLI) as a unique feature of optical imaging to quantitate in vivo drug-target engagement. MFLI quantitatively reports on nanoscale interactions via lifetime-sensing of Förster Resonance Energy Transfer (FRET) in live, intact animals. Hence, MFLI FRET acts as a direct reporter of receptor dimerization and target engagement via the measurement of the fraction of labeled-donor entity undergoing binding to its respective receptor. MFLI is expected to greatly impact preclinical imaging and also adjacent fields such as image-guided surgery and drug development.

Correntropy-induced metric with Laplacian kernel for robust fluorescence molecular tomography

Fluorescence molecular tomography (FMT), which is used to visualize the three-dimensional distribution of fluorescence probe in small animals via the reconstruction method, has become a promising imaging technique in preclinical research. However, the classical reconstruction criterion is formulated based on the squared l ₂-norm distance metric, leaving it prone to being influenced by the presence of outliers. In this study, we propose a robust distance based on the correntropy-induced metric with a Laplacian kernel (CIML). The proposed metric satisfies the conditions of distance metric function and contains first and higher order moments of samples. Moreover, we demonstrate important properties of the proposed metric such as nonnegativity, nonconvexity, and boundedness, and analyze its robustness from the perspective of M-estimation. The proposed metric includes and extends the traditional metrics such as l ₀-norm and l ₁-norm metrics by setting an appropriate parameter. We show that, in reconstruction, the metric is a sparsity-promoting penalty. To reduce the negative effects of noise and outliers, a novel robust reconstruction framework is presented with the proposed correntropy-based metric. The proposed CIML model retains the advantages of the traditional model and promotes robustness. However, the nonconvexity of the proposed metric renders the CIML model difficult to optimize. Furthermore, an effective iterative algorithm for the CIML model is designed, and we present a theoretical analysis of its ability to converge. Numerical simulation and in vivo mouse experiments were conducted to evaluate the CIML method's performance. The experimental results show that the proposed method achieved more accurate fluorescent target reconstruction than the state-of-the-art methods in most cases, which illustrates the feasibility and robustness of the CIML method.

Deep Learning in Biomedical Optics

This article reviews deep learning applications in biomedical optics with a particular emphasis on image formation. The review is organized by imaging domains within biomedical optics and includes microscopy, fluorescence lifetime imaging, in vivo microscopy, widefield endoscopy, optical coherence tomography, photoacoustic imaging, diffuse tomography, and functional optical brain imaging. For each of these domains, we summarize how deep learning has been applied and highlight methods by which deep learning can enable new capabilities for optics in medicine. Challenges and opportunities to improve translation and adoption of deep learning in biomedical optics are also summarized. Lasers Surg. Med. © 2021 Wiley Periodicals LLC.

Recent progress in natural product-based inhibitor screening with enzymatic fluorescent probes

Drug discovery is a complex process in which many challenges need to be overcome, from the discovery of a drug candidate to ensuring the efficacy and safety of the candidate in humans. Modern analytical methods allow tens of thousands of drug candidates to be screened for their inhibition of specific enzymes or receptors. In recent years, fluorescent probes have been used for the detection and diagnosis of human pathogens as well as high-throughput screening. This review focuses on recent progress in organic small-molecule based enzyme-activated fluorescent probes for screening of inhibitors from natural products. The contents include the construction of fluorescent probes, working mechanism and the process of inhibitor screening. The progress suggests that fluorescent probes are a vital and rapidly growing technology for inhibitor screening of enzymes, in particular, inhibitor screening in situ.

Bayesian reconstruction of fluorescent molecular tomography via iteration of measurements

Fluorescent molecular tomography (FMT) is an important molecular imaging technique for medical diagnosis and treatment. In FMT, a typical forward model is the diffusion approximation. However, this approximation is not valid in biological tissues with low-scattering regions. To overcome this problem, a Bayesian method in combination with the model error is proposed. Further, an iteration method of boundary measurements is incorporated into the reconstruction process to improve the efficiency of reconstruction for FMT. Simulation results obtained demonstrate that the proposed approach can effectively improve the quality of the reconstructed results and speed up the reconstruction process.

Bioluminescence Imaging Applications in Cancer: A Comprehensive Review

Bioluminescence imaging (BLI), an optical preclinical imaging modality, is an invaluable imaging modality due to its low-cost, high throughput, fast acquisition times, and functional imaging capabilities. BLI is being extensively used in the field of cancer imaging, especially with the recent developments in genetic-engineering, stem cell, and gene therapy treatments. The purpose of this paper is to provide a comprehensive review of the principles, developments, and current status of BLI in cancer research. This paper covers the fundamental BLI concepts including BLI reporters and enzyme-substrate systems, data acquisition, and image characteristics. It reviews the studies discussing the use of BLI in cancer research such as imaging tumor-characteristic phenomena including tumorigenesis, metastasis, cancer metabolism, apoptosis, hypoxia, and angiogenesis, and response to cancer therapy treatments including chemotherapy, radiotherapy, immunotherapy, gene therapy, and stem cell therapy. The key advantages and disadvantages of BLI compared to other common imaging modalities are also discussed.

Early-photon reflectance fluorescence molecular tomography for small animal imaging: Mathematical model and numerical experiment

The paper presents an original approach to time-domain reflectance fluorescence molecular tomography (FMT) of small animals. It is based on the use of early arriving photons and state-of-the-art compressed-sensing-like reconstruction algorithms and aims to improve the spatial resolution of fluorescent images. We deduce the fundamental equation that models the imaging operator and derive analytical representations for the sensitivity functions which are responsible for the reconstruction of the fluorophore absorption coefficient. The idea of fluorescence lifetime tomography with our approach is also discussed. We conduct a numerical experiment on 3D reconstruction of box phantoms with spherical fluorescent inclusions of small diameters. For modeling measurement data and constructing the sensitivity matrix we assume a virtual fluorescence tomograph with a scanning fiber probe that illuminates and collects light in reflectance geometry. It provides for large source-receiver separations which correspond to the macroscopic regime. Two compressed-sensing-like reconstruction algorithms are used to solve the inverse problem. These are the algebraic reconstruction technique with total variation regularization and our modification of the fast iterative shrinkage-thresholding algorithm. Results of our numerical experiment show that our approach is capable of achieving as good spatial resolution as 0.2 mm and even better at depths to 9 mm inclusive.

Evaluation of temporal moments and Fourier transformed data in time-domain diffuse optical tomography

Time-domain diffuse optical tomography (TD-DOT) uses near-infrared pulsed lasers as light sources to measure time-varying exitance on the boundary of the target. These are used to estimate optical properties of the imaged target. Several integral-transform-based moments of the time-resolved data have been utilized in TD-DOT, the most common being the mean time of flight and variance. Recently, it has been shown that Fourier transforming the time-domain data to frequency domain enables utilization of these data at one or several frequencies, producing equally as good estimates as the whole time-domain data. In this work, we present a systematic comparison of the usage of the temporal moments and Fourier transformed data in TD-DOT. Both absolute and difference imaging are evaluated using numerical simulations. The simulations show that utilizing temporal moments and Fourier transformed data in TD-DOT provides good quality reconstructions with a good estimation accuracy. These estimates are improved if more than one data type is used. Furthermore, the simulations show that the frequency-domain computations enable computationally cheaper and straightforward implementation of the inverse solver when compared to the temporal moments.

Adaptive shrinking reconstruction framework for cone-beam X-ray luminescence computed tomography

Cone-beam X-ray luminescence computed tomography (CB-XLCT) emerged as a novel hybrid technique for early detection of small tumors in vivo. However, severe ill-posedness is still a challenge for CB-XLCT imaging. In this study, an adaptive shrinking reconstruction framework without a prior information is proposed for CB-XLCT. In reconstruction processing, the mesh nodes are automatically selected with higher probability to contribute to the distribution of target for imaging. Specially, an adaptive shrinking function is designed to automatically control the permissible source region at a multi-scale rate. Both 3D digital mouse and in vivo experiments were carried out to test the performance of our method. The results indicate that the proposed framework can dramatically improve the imaging quality of CB-XLCT.

Rising role of artificial intelligence in image reconstruction for biomedical imaging

In this editorial, we review recent progress on the applications of artificial intelligence (AI) in image reconstruction for biomedical imaging. Because it abandons prior information of traditional artificial design and adopts a completely data-driven mode to obtain deeper prior information via learning, AI technology plays an increasingly important role in biomedical image reconstruction. The combination of AI technology and the biomedical image reconstruction method has become a hotspot in the field. Favoring AI, the performance of biomedical image reconstruction has been improved in terms of accuracy, resolution, imaging speed, etc. We specifically focus on how to use AI technology to improve the performance of biomedical image reconstruction, and propose possible future directions in this field.

Multimodal imaging combining time-domain near-infrared optical tomography and continuous-wave fluorescence molecular tomography

Fluorescence molecular tomography (FMT) emerges as a powerful non-invasive imaging tool with the ability to resolve fluorescence signals from sources located deep in living tissues. Yet, the accuracy of FMT reconstruction depends on the deviation of the assumed optical properties from the actual values. In this work, we improved the accuracy of the initial optical properties required for FMT using a new-generation time-domain (TD) near-infrared optical tomography (NIROT) system, which effectively decouples scattering and absorption coefficients. We proposed a multimodal paradigm combining TD-NIROT and continuous-wave (CW) FMT. Both numerical simulation and experiments were performed on a heterogeneous phantom containing a fluorescent inclusion. The results demonstrate significant improvement in the FMT reconstruction by taking the NIROT-derived optical properties as prior information. The multimodal method is attractive for preclinical studies and tumor diagnostics since both functional and molecular information can be obtained.

Smart Toolkit for Fluorescence Tomography: Simulation, Reconstruction, and Validation

OBJECTIVE

Fluorescence molecular tomography (FMT) can provide valuable molecular information by mapping the bio-distribution of fluorescent reporter molecules in the intact organism. Various prototype FMT systems have been introduced during the past decade. However, none of them has evolved as a standard tool for routine biomedical research. The goal of this paper is to develop a software package that can automate the complete FMT reconstruction procedure.

METHODS

We present smart toolkit for fluorescence tomography (STIFT), a comprehensive platform comprising three major protocols: 1) virtual FMT, i.e., forward modeling and reconstruction of simulated data; 2) control of actual FMT data acquisition; and 3) reconstruction of experimental FMT data.

RESULTS

Both simulation and phantom experiments have shown robust reconstruction results for homogeneous and heterogeneous tissue-mimicking phantoms containing fluorescent inclusions.

CONCLUSION

STIFT can be used for optimization of FMT experiments, in particular for optimizing illumination patterns.

SIGNIFICANCE

This paper facilitates FMT experiments by bridging the gaps between simulation, actual experiments, and data reconstruction.

A thermo-sensitive fluorescent agent based method for excitation light leakage rejection for fluorescence molecular tomography

Fluorescence molecular tomography (FMT) is widely used in preclinical oncology research. FMT is the only imaging technique able to provide 3D distribution of fluorescent probes within thick highly scattering media. However, its integration into clinical medicine has been hampered by its low spatial resolution caused by the undetermined and ill-posed nature of its reconstruction algorithm. Another major factor degrading the quality of FMT images is the large backscattered excitation light component leaking through the rejection filters and coinciding with the weak fluorescent signal arising from a low tissue fluorescence concentration. In this paper, we present a new method based on the use of a novel thermo-sensitive fluorescence probe. In fact, the excitation light leakage is accurately estimated from a set of measurements performed at different temperatures and then is corrected for in the tomographic data. The obtained results show a considerable improvement in both spatial resolution and quantitative accuracy of FMT images due to the proper correction of fluorescent signals.

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.

Hyperspectral wide-field time domain single-pixel diffuse optical tomography platform

We present the design and comprehensive instrumental characterization of a time domain diffuse optical tomography (TD-DOT) platform based on wide-field illumination and wide-field hyperspectral time-resolved single-pixel detection for functional and molecular imaging in turbid media. The proposed platform combines two digital micro-mirror devices (DMDs) to generate structured light and a spectrally resolved multi-anode photomultiplier tube (PMT) detector in time domain for hyperspectral data acquisition over 16 wavelength channels based on the time-correlated single-photon counting (TCSPC) technique. The design of the proposed platform is described in detail and its characteristics in spatial, temporal and spectral dimensions are calibrated and presented. The performance of the system is further validated through a phantom study where two absorbers in glass tubes with spectral contrast are mapped in a turbid medium of ~20 mm thickness. The method presented here offers the potential of accelerating the imaging process and improving reconstruction results in TD-DOT and thus facilitates its wide spread use in preclinical and clinical in vivo imaging scenarios.

Three-way decision based reconstruction frame for fluorescence molecular tomography

Fluorescence molecular tomography (FMT) has been a promising imaging tool because it allows an accurate localizaton and quantitative analysis of the fluorophore distribution in animals. It, however, is still a challenge since its reconstruction suffers from severe ill-posedness. This paper introduces a reconstruction frame based on three-way decisions (TWD) for the inverse problem of FMT. On the first stage, a reconstruction result on the whole region is obtained by a certain reconstruction algorithm. With TWD, the recovered result has been divided into three regions: fluorescent target region, boundary region, and background region. On the second stage, the boundary region and fluorescent target region have been combined into the permissible region of the target. Then a new reconstruction on the permissible region has been carried out and a new recovered result is obtained. With TWD again, the new result has been classified into three pairwise disjoint regions. And the new fluorescent target region is the final reconstructed result. Both numerical simulation experiments and a real mouse experiment are carried out to validate the feasibility and potential of the presented reconstruction frame. The results indicate that the proposed reconstuction strategy based on TWD can provide a good performance in FMT reconstruction.

Automated quantification of bioluminescence images

We developed a computer-aided analysis tool for quantitatively determining bioluminescent reporter distributions inside small animals. The core innovations are a body-fitting animal shuttle and a statistical mouse atlas, both of which are spatially aligned and scaled according to the animal’s weight, and hence provide data congruency across animals of varying size and pose. In conjunction with a multispectral bioluminescence tomography technique capitalizing on the spatial framework of the shuttle, the in vivo biodistribution of luminescent reporters can rapidly be calculated and, thus, enables operator-independent and computer-driven data analysis. We demonstrate its functionality by quantitatively monitoring a bacterial infection, where the bacterial organ burden was determined and validated with the established serial-plating method. In addition, the statistical mouse atlas was validated and compared to existing techniques providing an anatomical reference. The proposed data analysis tool promises to increase data throughput and data reproducibility and accelerate human disease modeling in mice.

Analysis of bioluminescence images of bacterial distributions in living animals is mostly manual and semiquantitative. Here, the authors present an analysis platform featuring an animal mold, a probabilistic organ atlas, and a mirror gantry to perform automatic in vivo bioluminescence quantification.

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.

Quantitative bioluminescence tomography using spectral derivative data

Bioluminescence imaging (BLI) is a non-contact, optical imaging technique based on measurement of emitted light due to an internal source, which is then often directly related to cellular activity. It is widely used in pre-clinical small animal imaging studies to assess the progression of diseases such as cancer, aiding in the development of new treatments and therapies. For many applications, the quantitative assessment of accurate cellular activity and spatial distribution is desirable as it would enable direct monitoring for prognostic evaluation. This requires quantitative spatially-resolved measurements of bioluminescence source strength inside the animal to be obtained from BLI images. This is the goal of bioluminescence tomography (BLT) in which a model of light propagation through tissue is combined with an optimization algorithm to reconstruct a map of the underlying source distribution. As most models consider only the propagation of light from internal sources to the animal skin surface, an additional challenge is accounting for the light propagation from the skin to the optical detector (e.g. camera). Existing approaches typically use a model of the imaging system optics (e.g. ray-tracing, analytical optical models) or approximate corrections derived from calibration measurements. However, these approaches are typically computationally intensive or of limited accuracy. In this work, a new approach is presented in which, rather than directly using BLI images acquired at several wavelengths, the spectral derivative of that data (difference of BLI images at adjacent wavelengths) is used in BLT. As light at similar wavelengths encounters a near-identical system response (path through the optics etc.) this eliminates the need for additional corrections or system models. This approach is applied to BLT with simulated and experimental phantom data and shown that the error in reconstructed source intensity is reduced from 49% to 4%. Qualitatively, the accuracy of source localization is improved in both simulated and experimental data, as compared to reconstruction using the standard approach. The outlined algorithm can widely be adapted to all commercial systems without any further technological modifications.

Bioluminescence tomography with structural information estimated via statistical mouse atlas registration

Due to an ill-posed and underestimated characteristic of bioluminescence tomography (BLT) reconstruction, a priori anatomical information obtained from computed tomography (CT) or magnetic resonance imaging (MRI), is usually incorporated to improve the reconstruction accuracy. The organs need to be segmented, which is time-consuming and challenging, especially for the low-contrast CT images. In this paper, we present a BLT reconstruction method based on a statistical mouse atlas to improve the efficiency of heterogeneous model generation and the accuracy of target localization. The low-contrast CT image of the mouse was first registered to the statistical mouse atlas model with the constraints of mouse surface and high-contrast organs (bone and lung). Then the other organs, such as the liver and kidney, were determined automatically through the statistical mouse atlas model. The estimated organs were then discretized into tetrahedral meshes for BLT reconstruction. The linearized Bregman method was used to solve the sparse inverse problem of BLT by minimizing the regularization function (L1 norm plus L2 norm with smooth factor). Both numerical simulations and in vivo experiments were conducted, and the results demonstrate that even though the localization of the estimated organs may not be exactly accurate, the proposed method is feasible to reconstruct the bioluminescent source effectively and accurately with the estimated organs. This method would greatly benefit the bioluminescent light source localization for hybrid BLT/CT systems.