A mouse optical simulation environment (MOSE) to investigate bioluminescent phenomena in the living mouse with the Monte Carlo method

Sparse reconstruction based on dictionary learning and group structure strategy for cone-beam X-ray luminescence computed tomography

As a dual-modal imaging technology that has emerged in recent years, cone-beam X-ray luminescence computed tomography (CB-XLCT) has exhibited promise as a tool for the early three-dimensional detection of tumors in small animals. However, due to the challenges imposed by the low absorption and high scattering of light in tissues, the CB-XLCT reconstruction problem is a severely ill-conditioned inverse problem, rendering it difficult to obtain satisfactory reconstruction results. In this study, a strategy that utilizes dictionary learning and group structure (DLGS) is proposed to achieve satisfactory CB-XLCT reconstruction performance. The group structure is employed to account for the clustering of nanophosphors in specific regions within the organism, which can enhance the interrelation of elements in the same group. Furthermore, the dictionary learning strategy is implemented to effectively capture sparse features. The performance of the proposed method was evaluated through numerical simulations and in vivo experiments. The experimental results demonstrate that the proposed method achieves superior reconstruction performance in terms of location accuracy, target shape, robustness, dual-source resolution, and in vivo practicability.

A Monte Carlo simulation for Moving Light Source in Intracavity PDT

We developed a simulation method for modeling the light fluence delivery in intracavity Photodynamic Therapy (icav-PDT) for pleural lung cancer using a moving light source. Due to the large surface area of the pleural lung cavity, the light source needs to be moved to deliver a uniform dose around the entire cavity. While multiple fixed detectors are used for dosimetry at a few locations, an accurate simulation of light fluence and fluence rate is still needed for the rest of the cavity. We extended an existing Monte Carlo (MC) based light propagation solver to support moving light sources by densely sampling the continuous light source trajectory and assigning the proper number of photon packages launched along the way. The performance of Simphotek GPU CUDA-based implementation of the method - PEDSy-MC - has been demonstrated on a life-size lung-shaped phantom, custom printed for testing icav-PDT navigation system at the Perlman School of Medicine (PSM) - calculations completed under a minute (for some cases) and within minutes have been achieved. We demonstrate results within a 5% error of the analytic solution for multiple detectors in the phantom. PEDSy-MC is accompanied by a dose-cavity visualization tool that allows real-time inspection of dose values of the treated cavity in 2D and 3D, which will be expanded to ongoing clinical trials at PSM. PSM has developed a technology to measure 8-detectors in a pleural cavity phantom using Photofrin-mediated PDT that has been used during validation.

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.

Meshless Monte Carlo radiation transfer method for curved geometries using signed distance functions

SIGNIFICANCE

Monte Carlo radiation transfer (MCRT) is the gold standard for modeling light transport in turbid media. Typical MCRT models use voxels or meshes to approximate experimental geometry. A voxel-based geometry does not allow for the precise modeling of smooth curved surfaces, such as may be found in biological systems or food and drink packaging. Mesh-based geometry allows arbitrary complex shapes with smooth curved surfaces to be modeled. However, mesh-based models also suffer from issues such as the computational cost of generating meshes and inaccuracies in how meshes handle reflections and refractions.

AIM

We present our algorithm, which we term signedMCRT (sMCRT), a geometry-based method that uses signed distance functions (SDF) to represent the geometry of the model. SDFs are capable of modeling smooth curved surfaces precisely while also modeling complex geometries.

APPROACH

We show that using SDFs to represent the problem's geometry is more precise than voxel and mesh-based methods.

RESULTS

sMCRT is validated against theoretical expressions, and voxel and mesh-based MCRT codes. We show that sMCRT can precisely model arbitrary complex geometries such as microvascular vessel network using SDFs. In comparison with the current state-of-the-art in MCRT methods specifically for curved surfaces, sMCRT is more precise for cases where the geometry can be defined using combinations of shapes.

CONCLUSIONS

We believe that SDF-based MCRT models are a complementary method to voxel and mesh models in terms of being able to model complex geometries and accurately treat curved surfaces, with a focus on precise simulation of reflections and refractions. sMCRT is publicly available at https://github.com/lewisfish/signedMCRT.

Probability risk assessment of soil PAH contamination premised on industrial brownfield development: a case from China

The harm of polycyclic aromatic hydrocarbons to human health and the natural environment has become an indisputable fact. Compared with other pollutants, PAHs are more toxic at low environmental concentrations, especially in industrialized environments. This study investigated the concentration distribution of soil PAHs at a well-known industrial production site in China and applied the Monte Carlo simulation method to assess the risk of cancer caused by the excessive accidental intake of PAHs in brownfield development environments. The results showed that the PAH content of the soil at the study site exceeded the local soil quality background value to varying degrees, and the excess rate ranged from 0.72 to 22.3%. There are serious health risks of BaP at the site, which has a 95th health risk percentile value of 1.12E-04. Those for BbF, InP, and DBA range from 1.0×10^-6 to 1.0×10^-4, and potential health risks occur. Moreover, the exposure duration and average carcinogenic time were the most influential parameters. The study has revealed that exposure to brownfield soil contaminated with PAHs increases the health risks. This is a representative study on the occurrence and concentration of PAHs in industrial brownfields in China, which can be adopted as a basis and evidence for pollution risk assessment of brownfield development.

Harnessing the Power of Hybrid Light Propagation Model for Three-Dimensional Optical Imaging in Cancer Detection

Optical imaging is an emerging technology capable of qualitatively and quantitatively observing life processes at the cellular or molecular level and plays a significant role in cancer detection. In particular, to overcome the disadvantages of traditional optical imaging that only two-dimensionally and qualitatively detect biomedical information, the corresponding three-dimensional (3D) imaging technology is intensively explored to provide 3D quantitative information, such as localization and distribution and tumor cell volume. To retrieve these information, light propagation models that reflect the interaction between light and biological tissues are an important prerequisite and basis for 3D optical imaging. This review concentrates on the recent advances in hybrid light propagation models, with particular emphasis on their powerful use for 3D optical imaging in cancer detection. Finally, we prospect the wider application of the hybrid light propagation model and future potential of 3D optical imaging in cancer detection.

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.

A novel Cerenkov luminescence tomography approach using multilayer fully connected neural network

Cerenkov luminescence tomography (CLT) has been proved as an effective tool for various biomedical applications. Because of the severe scattering of Cerenkov luminescence, the performance of CLT remains unsatisfied. This paper proposed a novel CLT reconstruction approach based on a multilayer fully connected neural network (MFCNN). Monte Carlo simulation data was employed to train the MFCNN, and the complex relationship between the surface signals and the true sources was effectively learned by the network. Both simulation and in vivo experiments were performed to validate the performance of MFCNN CLT, and it was further compared with the typical radiative transfer equation (RTE) based method. The experimental data showed the superiority of MFCNN CLT in terms of accuracy and stability. This promising approach for CLT is expected to improve the performance of optical tomography, and to promote the exploration of machine learning in biomedical applications.

FullMonteCUDA: a fast, flexible, and accurate GPU-accelerated Monte Carlo simulator for light propagation in turbid media

Optimizing light delivery for photodynamic therapy, quantifying tissue optical properties or reconstructing 3D distributions of sources in bioluminescence imaging and absorbers in diffuse optical imaging all involve solving an inverse problem. This can require thousands of forward light propagation simulations to determine the parameters to optimize treatment, image tissue or quantify tissue optical properties, which is time-consuming and computationally expensive. Addressing this problem requires a light propagation simulator that produces results quickly given modelling parameters. In previous work, we developed FullMonteSW: currently the fastest, tetrahedral-mesh, Monte Carlo light propagation simulator written in software. Additional software optimizations showed diminishing performance improvements, so we investigated hardware acceleration methods. This work focuses on FullMonteCUDA: a GPU-accelerated version of FullMonteSW which targets NVIDIA GPUs. FullMonteCUDA has been validated across several benchmark models and, through various GPU-specific optimizations, achieves a 288-936x speedup over the single-threaded, non-vectorized version of FullMonteSW and a 4-13x speedup over the highly optimized, hand-vectorized and multi-threaded version. The increase in performance allows inverse problems to be solved more efficiently and effectively.

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.

Optical model of the murine lung to optimize pulmonary illumination

We describe a Monte Carlo model of the mouse torso to optimize illumination of the mouse lung for fluorescence detection of low levels of pulmonary pathogens, specifically Mycobacterium tuberculosis. After validation of the simulation with an internally illuminated optical phantom, the entire mouse torso was simulated to compare external and internal illumination techniques. Measured optical properties of deflated mouse lungs were scaled to mimic the diffusive properties of inflated lungs in vivo. Using the full-torso model, a 2  ×   to 3  ×   improvement in average fluence rate in the lung was seen for dorsal compared with ventral positioning of the mouse with external illumination. The enhancement in average fluence rate in the lung using internal excitation was 40  ×   to 60  ×   over external illumination in the dorsal position. Parameters of the internal fiber optic source were manipulated in the model to guide optimization of the physical system and experimental protocol for internal illumination and whole-body detection of fluorescent mycobacteria in a mouse model of infection.

Lens implementation on the GATE Monte Carlo toolkit for optical imaging simulation

Optical imaging techniques are widely used for in vivo preclinical studies, and it is well known that the Geant4 Application for Emission Tomography (GATE) can be employed for the Monte Carlo (MC) modeling of light transport inside heterogeneous tissues. However, the GATE MC toolkit is limited in that it does not yet include optical lens implementation, even though this is required for a more realistic optical imaging simulation. We describe our implementation of a biconvex lens into the GATE MC toolkit to improve both the sensitivity and spatial resolution for optical imaging simulation. The lens implemented into the GATE was validated against the ZEMAX optical simulation using an US air force 1951 resolution target. The ray diagrams and the charge-coupled device images of the GATE optical simulation agreed with the ZEMAX optical simulation results. In conclusion, the use of a lens on the GATE optical simulation could improve the image quality of bioluminescence and fluorescence significantly as compared with pinhole optics.

Advances in Monte Carlo Simulation for Light Propagation in Tissue

Monte Carlo (MC) simulation for light propagation in tissue is the gold standard for studying the light propagation in biological tissue and has been used for years. Interaction of photons with a medium is simulated based on its optical properties. New simulation geometries, tissue-light interaction methods, and recording techniques recently have been designed. Applications, such as whole mouse body simulations for fluorescence imaging, eye modeling for blood vessel imaging, skin modeling for terahertz imaging, and human head modeling for sinus imaging, have emerged. Here, we review the technical advances and recent applications of MC simulation.

Monte Carlo simulation of radiation transport in human skin with rigorous treatment of curved tissue boundaries

In three-dimensional (3-D) modeling of light transport in heterogeneous biological structures using the Monte Carlo (MC) approach, space is commonly discretized into optically homogeneous voxels by a rectangular spatial grid. Any round or oblique boundaries between neighboring tissues thus become serrated, which raises legitimate concerns about the realism of modeling results with regard to reflection and refraction of light on such boundaries. We analyze the related effects by systematic comparison with an augmented 3-D MC code, in which analytically defined tissue boundaries are treated in a rigorous manner. At specific locations within our test geometries, energy deposition predicted by the two models can vary by 10%. Even highly relevant integral quantities, such as linear density of the energy absorbed by modeled blood vessels, differ by up to 30%. Most notably, the values predicted by the customary model vary strongly and quite erratically with the spatial discretization step and upon minor repositioning of the computational grid. Meanwhile, the augmented model shows no such unphysical behavior. Artifacts of the former approach do not converge toward zero with ever finer spatial discretization, confirming that it suffers from inherent deficiencies due to inaccurate treatment of reflection and refraction at round tissue boundaries.

Mesoscopic fluorescence tomography of a photosensitizer (HPPH) 3D biodistribution in skin cancer

RATIONALE AND OBJECTIVES

Photodynamic therapy (PDT) is a promising strategy for treating cancer. PDT involves three components: a photosensitizer (PS) drug, a specific wavelength of drug-activating light, and oxygen. A challenge in PDT is the unknown biodistribution of the PS in the target tissue. In this preliminary study, we report the development of a new approach to image in three dimensions the PS biodistribution in a noninvasive and fast manner.

MATERIALS AND METHODS

A mesoscopic fluorescence tomography imaging platform was used to image noninvasively the biodistribution of 2-[1-hexyloxyethyl]-2 devinyl pyropheophorbide-a (HPPH) in preclinical skin cancer models. Seven tumors were imaged and optical reconstructions were compared to nonconcurrent ultrasound data.

RESULTS

Successful imaging of the HPPH biodistribution was achieved on seven skin cancer tumors in preclinical models with a typical acquisition time of 1 minute. Two-dimensional fluorescence signals and estimated three-dimensional PS distributions were located within the lesions. However, HPPH distribution was highly heterogeneous with the tumors. Moreover, HPPH distribution volume and tumor volume as estimated by ultrasound did not match.

CONCLUSIONS

The results of this proof-of-concept study demonstrate the potential of MFMT to image rapidly the HPPH three-dimensional biodistribution in skin cancers. In addition, these preliminary data indicate that the PS biodistribution in skin cancer tumors is heterogeneous and does not match anatomical data. Mesoscopic fluorescence molecular tomography, by imaging fluorescence signals over large areas with high spatial sampling and at fast acquisition speeds, may be a new imaging modality of choice for planning and optimizing of PDT treatment.

Extension of the GATE Monte-Carlo simulation package to model bioluminescence and fluorescence imaging

The Geant4 Application for Emission Tomography (GATE) is an advanced open-source software dedicated to Monte-Carlo (MC) simulations in medical imaging involving photon transportation (Positron emission tomography, single photon emission computed tomography, computed tomography) and in particle therapy. In this work, we extend the GATE to support simulations of optical imaging, such as bioluminescence or fluorescence imaging, and validate it against the MC for multilayered media standard simulation tool for biomedical optics in simple geometries. A full simulation set-up for molecular optical imaging (bioluminescence and fluorescence) is implemented in GATE, and images of the light distribution emitted from a phantom demonstrate the relevance of using GATE for optical imaging simulations.

A Multi-Camera System for Bioluminescence Tomography in Preclinical Oncology Research

Bioluminescent imaging (BLI) of cells expressing luciferase is a valuable noninvasive technique for investigating molecular events and tumor dynamics in the living animal. Current usage is often limited to planar imaging, but tomographic imaging can enhance the usefulness of this technique in quantitative biomedical studies by allowing accurate determination of tumor size and attribution of the emitted light to a specific organ or tissue. Bioluminescence tomography based on a single camera with source rotation or mirrors to provide additional views has previously been reported. We report here in vivo studies using a novel approach with multiple rotating cameras that, when combined with image reconstruction software, provides the desired representation of point source metastases and other small lesions. Comparison with MRI validated the ability to detect lung tumor colonization in mouse lung.

Influence investigation of a void region on modeling light propagation in a heterogeneous medium

A void region exists in some biological tissues, and previous studies have shown that inaccurate images would be obtained if it were not processed. A hybrid radiosity-diffusion method (HRDM) that couples the radiosity theory and the diffusion equation has been proposed to deal with the void problem and has been well demonstrated in two-dimensional and three-dimensional (3D) simple models. However, the extent of the impact of the void region on the accuracy of modeling light propagation has not been investigated. In this paper, we first implemented and verified the HRDM in 3D models, including both the regular geometries and a digital mouse model, and then investigated the influences of the void region on modeling light propagation in a heterogeneous medium. Our investigation results show that the influence of the region can be neglected when the size of the void is less than a certain range, and other cases must be taken into account.

Light transport in turbid media with non-scattering, low-scattering and high absorption heterogeneities based on hybrid simplified spherical harmonics with radiosity model

Modeling light propagation in the whole body is essential and necessary for optical imaging. However, non-scattering, low-scattering and high absorption regions commonly exist in biological tissues, which lead to inaccuracy of the existing light transport models. In this paper, a novel hybrid light transport model that couples the simplified spherical harmonics approximation (SPN) with the radiosity theory (HSRM) was presented, to accurately describe light transport in turbid media with non-scattering, low-scattering and high absorption heterogeneities. In the model, the radiosity theory was used to characterize the light transport in non-scattering regions and the SPN was employed to handle the scattering problems, including subsets of low-scattering and high absorption. A Neumann source constructed by the light transport in the non-scattering region and formed at the interface between the non-scattering and scattering regions was superposed into the original light source, to couple the SPN with the radiosity theory. The accuracy and effectiveness of the HSRM was first verified with both regular and digital mouse model based simulations and a physical phantom based experiment. The feasibility and applicability of the HSRM was then investigated by a broad range of optical properties. Lastly, the influence of depth of the light source on the model was also discussed. Primary results showed that the proposed model provided high performance for light transport in turbid media with non-scattering, low-scattering and high absorption heterogeneities.

Accelerating mesh-based Monte Carlo method on modern CPU architectures

In this report, we discuss the use of contemporary ray-tracing techniques to accelerate 3D mesh-based Monte Carlo photon transport simulations. Single Instruction Multiple Data (SIMD) based computation and branch-less design are exploited to accelerate ray-tetrahedron intersection tests and yield a 2-fold speed-up for ray-tracing calculations on a multi-core CPU. As part of this work, we have also studied SIMD-accelerated random number generators and math functions. The combination of these techniques achieved an overall improvement of 22% in simulation speed as compared to using a non-SIMD implementation. We applied this new method to analyze a complex numerical phantom and both the phantom data and the improved code are available as open-source software at http://mcx.sourceforge.net/mmc/.

Total variation regularization for bioluminescence tomography with the split Bregman method

Regularization methods have been broadly applied to bioluminescence tomography (BLT) to obtain stable solutions, including l2 and l1 regularizations. However, l2 regularization can oversmooth reconstructed images and l1 regularization may sparsify the source distribution, which degrades image quality. In this paper, the use of total variation (TV) regularization in BLT is investigated. Since a nonnegativity constraint can lead to improved image quality, the nonnegative constraint should be considered in BLT. However, TV regularization with a nonnegativity constraint is extremely difficult to solve due to its nondifferentiability and nonlinearity. The aim of this work is to validate the split Bregman method to minimize the TV regularization problem with a nonnegativity constraint for BLT. The performance of split Bregman-resolved TV (SBRTV) based BLT reconstruction algorithm was verified with numerical and in vivo experiments. Experimental results demonstrate that the SBRTV regularization can provide better regularization quality over l2 and l1 regularizations.

Comparisons of hybrid radiosity-diffusion model and diffusion equation for bioluminescence tomography in cavity cancer detection

Bioluminescence tomography (BLT) has been successfully applied to the detection and therapeutic evaluation of solid cancers. However, the existing BLT reconstruction algorithms are not accurate enough for cavity cancer detection because of neglecting the void problem. Motivated by the ability of the hybrid radiosity-diffusion model (HRDM) in describing the light propagation in cavity organs, an HRDM-based BLT reconstruction algorithm was provided for the specific problem of cavity cancer detection. HRDM has been applied to optical tomography but is limited to simple and regular geometries because of the complexity in coupling the boundary between the scattering and void region. In the provided algorithm, HRDM was first applied to three-dimensional complicated and irregular geometries and then employed as the forward light transport model to describe the bioluminescent light propagation in tissues. Combining HRDM with the sparse reconstruction strategy, the cavity cancer cells labeled with bioluminescent probes can be more accurately reconstructed. Compared with the diffusion equation based reconstruction algorithm, the essentiality and superiority of the HRDM-based algorithm were demonstrated with simulation, phantom and animal studies. An in vivo gastric cancer-bearing nude mouse experiment was conducted, whose results revealed the ability and feasibility of the HRDM-based algorithm in the biomedical application of gastric cancer detection.

An adaptive regularization parameter choice strategy for multispectral bioluminescence tomography

PURPOSE

Bioluminescence tomography (BLT) provides an effective tool for monitoring physiological and pathological activities in vivo. However, the measured data in bioluminescence imaging are corrupted by noise. Therefore, regularization methods are commonly used to find a regularized solution. Nevertheless, for the quality of the reconstructed bioluminescent source obtained by regularization methods, the choice of the regularization parameters is crucial. To date, the selection of regularization parameters remains challenging. With regards to the above problems, the authors proposed a BLT reconstruction algorithm with an adaptive parameter choice rule.

METHODS

The proposed reconstruction algorithm uses a diffusion equation for modeling the bioluminescent photon transport. The diffusion equation is solved with a finite element method. Computed tomography (CT) images provide anatomical information regarding the geometry of the small animal and its internal organs. To reduce the ill-posedness of BLT, spectral information and the optimal permissible source region are employed. Then, the relationship between the unknown source distribution and multiview and multispectral boundary measurements is established based on the finite element method and the optimal permissible source region. Since the measured data are noisy, the BLT reconstruction is formulated as l(2) data fidelity and a general regularization term. When choosing the regularization parameters for BLT, an efficient model function approach is proposed, which does not require knowledge of the noise level. This approach only requests the computation of the residual and regularized solution norm. With this knowledge, we construct the model function to approximate the objective function, and the regularization parameter is updated iteratively.

RESULTS

First, the micro-CT based mouse phantom was used for simulation verification. Simulation experiments were used to illustrate why multispectral data were used rather than monochromatic data. Furthermore, the study conducted using an adaptive regularization parameter demonstrated our ability to accurately localize the bioluminescent source. With the adaptively estimated regularization parameter, the reconstructed center position of the source was (20.37, 31.05, 12.95) mm, and the distance to the real source was 0.63 mm. The results of the dual-source experiments further showed that our algorithm could localize the bioluminescent sources accurately. The authors then presented experimental evidence that the proposed algorithm exhibited its calculated efficiency over the heuristic method. The effectiveness of the new algorithm was also confirmed by comparing it with the L-curve method. Furthermore, various initial speculations regarding the regularization parameter were used to illustrate the convergence of our algorithm. Finally, in vivo mouse experiment further illustrates the effectiveness of the proposed algorithm.

CONCLUSIONS

Utilizing numerical, physical phantom and in vivo examples, we demonstrated that the bioluminescent sources could be reconstructed accurately with automatic regularization parameters. The proposed algorithm exhibited superior performance than both the heuristic regularization parameter choice method and L-curve method based on the computational speed and localization error.

Development of molecular imaging and nanomedicine in China

The rapid progress of molecular imaging (MI) and the application of nanotechnology in medicine have the potential to advance the foundations of diagnosis, treatment, and prevention of diseases. Although MI and biomedical nanotechnology are still in a formative phase in China, much has been achieved over the last decade. This article provides a commentary on the development and current status of nanomedicine in China, with a selective focus on Chinese nanoparticle synthesis technology, the development of imaging equipment, and the preclinical application of novel MI probes.

Comparison of permissible source region and multispectral data using efficient bioluminescence tomography method

As a novel molecular imaging technology, bioluminescence tomography (BLT) has become an important tool for biomedical research in recent years, which can perform a quantitative reconstruction of an internal light source distribution with the scattered and transmitted bioluminescent signals measured on the external surface of a small animal. However, BLT is severely ill-posed because of complex photon propagation in the biological tissue and limited boundary measured data with noise. Therefore, sufficient a priori knowledge should be fused for the uniqueness and stability of BLT solution. Permissible source region strategy and spectrally resolved measurements are two kinds of a priori knowledge commonly used in BLT reconstruction. This paper compares their performance with simulation and in vivo heterogeneous mouse experiments. In order to improve the efficiency of large-scale source restoration, this paper introduces an efficient iterative shrinkage thresholding method that not only has faster convergence rate but also has better reconstruction accuracy than the modified Newton-type optimization approach. Finally, a discussion of these two kinds of a priori knowledge is given based on the comparison results.

Molecular image segmentation based on improved fuzzy clustering

Segmentation of molecular images is a difficult task due to the low signal-to-noise ratio of images. A novel two-dimensional fuzzy C-means (2DFCM) algorithm is proposed for the molecular image segmentation. The 2DFCM algorithm is composed of three stages. The first stage is the noise suppression by utilizing a method combining a Gaussian noise filter and anisotropic diffusion techniques. The second stage is the texture energy characterization using a Gabor wavelet method. The third stage is introducing spatial constraints provided by the denoising data and the textural information into the two-dimensional fuzzy clustering. The incorporation of intensity and textural information allows the 2DFCM algorithm to produce satisfactory segmentation results for images corrupted by noise (outliers) and intensity variations. The 2DFCM can achieve 0.96 +/- 0.03 segmentation accuracy for synthetic images under different imaging conditions. Experimental results on a real molecular image also show the effectiveness of the proposed algorithm.

Tomographic bioluminescence imaging reconstruction via a dynamically sparse regularized global method in mouse models

Generally, the performance of tomographic bioluminescence imaging is dependent on several factors, such as regularization parameters and initial guess of source distribution. In this paper, a global-inexact-Newton based reconstruction method, which is regularized by a dynamic sparse term, is presented for tomographic reconstruction. The proposed method can enhance higher imaging reliability and efficiency. In vivo mouse experimental reconstructions were performed to validate the proposed method. Reconstruction comparisons of the proposed method with other methods demonstrate the applicability on an entire region. Moreover, the reliable performance on a wide range of regularization parameters and initial unknown values were also investigated. Based on the in vivo experiment and a mouse atlas, the tolerance for optical property mismatch was evaluated with optical overestimation and underestimation. Additionally, the reconstruction efficiency was also investigated with different sizes of mouse grids. We showed that this method was reliable for tomographic bioluminescence imaging in practical mouse experimental applications.

A study on tetrahedron-based inhomogeneous Monte Carlo optical simulation

Monte Carlo (MC) simulation is widely recognized as a gold standard in biophotonics for its high accuracy. Here we analyze several issues associated with tetrahedron-based optical Monte Carlo simulation in the context of TIM-OS, MMCM, MCML, and CUDAMCML in terms of accuracy and efficiency. Our results show that TIM-OS has significant better performance in the complex geometry cases and has comparable performance with CUDAMCML in the multi-layered tissue model.

Generalized free-space diffuse photon transport model based on the influence analysis of a camera lens diaphragm

The camera lens diaphragm is an important component in a noncontact optical imaging system and has a crucial influence on the images registered on the CCD camera. However, this influence has not been taken into account in the existing free-space photon transport models. To model the photon transport process more accurately, a generalized free-space photon transport model is proposed. It combines Lambertian source theory with analysis of the influence of the camera lens diaphragm to simulate photon transport process in free space. In addition, the radiance theorem is also adopted to establish the energy relationship between the virtual detector and the CCD camera. The accuracy and feasibility of the proposed model is validated with a Monte-Carlo-based free-space photon transport model and physical phantom experiment. A comparison study with our previous hybrid radiosity-radiance theorem based model demonstrates the improvement performance and potential of the proposed model for simulating photon transport process in free space.

Evaluation of the simplified spherical harmonics approximation in bioluminescence tomography through heterogeneous mouse models

In vivo bioluminescence imaging (BLI) has played a more and more important role in biomedical research of small animals. Bioluminescence tomography (BLT) further translates the BLI optical information into three-dimensional bioluminescent source distribution, which could greatly facilitate applications in related studies. Although the diffusion approximation (DA) is one of the most widely-used forward models, higher-order approximations are still needed for in vivo small animal imaging. In this work, as a relatively accurate and higher-order approximation theory, the performance of the simplified spherical harmonics approximation (SPN) in BLT is evaluated detailedly in heterogeneous small animals. In the numerical validations, the SPN based results demonstrate better imaging quality compared with diffusion approximation heterogeneously under various source locations over wide optical domain. Although the evaluation for the effects of the optical property mismatch indicates the sensitivity of SPN is similar with DA model in the source localization, it may offer improved performance with much less artifacts. In what follows, heterogeneous experimental BLT reconstructions using in vivo mouse further evaluate the capability of the higher-order method for practical biomedical applications.

3D reconstruction of light flux distribution on arbitrary surfaces from 2D multi-photographic images

Optical tomography can demonstrate accurate three-dimensional (3D) imaging that recovers the 3D spatial distribution and concentration of the luminescent probes in biological tissues, compared with planar imaging. However, the tomographic approach is extremely difficult to implement due to the complexity in the reconstruction of 3D surface flux distribution from multi-view two dimensional (2D) measurements on the subject surface. To handle this problem, a novel and effective method is proposed in this paper to determine the surface flux distribution from multi-view 2D photographic images acquired by a set of non-contact detectors. The method is validated with comparison experiments involving both regular and irregular surfaces. Reconstruction of the inside probes based on the reconstructed surface flux distribution further demonstrates the potential of the proposed method in its application in optical tomography.

The CUBLAS and CULA based GPU acceleration of adaptive finite element framework for bioluminescence tomography

In molecular imaging (MI), especially the optical molecular imaging, bioluminescence tomography (BLT) emerges as an effective imaging modality for small animal imaging. The finite element methods (FEMs), especially the adaptive finite element (AFE) framework, play an important role in BLT. The processing speed of the FEMs and the AFE framework still needs to be improved, although the multi-thread CPU technology and the multi CPU technology have already been applied. In this paper, we for the first time introduce a new kind of acceleration technology to accelerate the AFE framework for BLT, using the graphics processing unit (GPU). Besides the processing speed, the GPU technology can get a balance between the cost and performance. The CUBLAS and CULA are two main important and powerful libraries for programming on NVIDIA GPUs. With the help of CUBLAS and CULA, it is easy to code on NVIDIA GPU and there is no need to worry about the details about the hardware environment of a specific GPU. The numerical experiments are designed to show the necessity, effect and application of the proposed CUBLAS and CULA based GPU acceleration. From the results of the experiments, we can reach the conclusion that the proposed CUBLAS and CULA based GPU acceleration method can improve the processing speed of the AFE framework very much while getting a balance between cost and performance.

Qualitative simulation of photon transport in free space based on monte carlo method and its parallel implementation

During the past decade, Monte Carlo method has obtained wide applications in optical imaging to simulate photon transport process inside tissues. However, this method has not been effectively extended to the simulation of free-space photon transport at present. In this paper, a uniform framework for noncontact optical imaging is proposed based on Monte Carlo method, which consists of the simulation of photon transport both in tissues and in free space. Specifically, the simplification theory of lens system is utilized to model the camera lens equipped in the optical imaging system, and Monte Carlo method is employed to describe the energy transformation from the tissue surface to the CCD camera. Also, the focusing effect of camera lens is considered to establish the relationship of corresponding points between tissue surface and CCD camera. Furthermore, a parallel version of the framework is realized, making the simulation much more convenient and effective. The feasibility of the uniform framework and the effectiveness of the parallel version are demonstrated with a cylindrical phantom based on real experimental results.

A trust region method in adaptive finite element framework for bioluminescence tomography

Bioluminescence tomography (BLT) is an effective molecular imaging (MI) modality. Because of the ill-posedness, the inverse problem of BLT is still open. We present a trust region method (TRM) for BLT source reconstruction. The TRM is applied in the source reconstruction procedure of BLT for the first time. The results of both numerical simulations and the experiments of cube phantom and nude mouse draw us to the conclusion that based on the adaptive finite element (AFE) framework, the TRM works in the source reconstruction procedure of BLT. To make our conclusion more reliable, we also compare the performance of the TRM and that of the famous Tikhonov regularization method after only one step of mesh refinement of the AFE framework. The conclusion is that the TRM can get faster and better results after only one mesh refinement step of AFE framework than the Tikhonov regularization method when handling large scale data. In the TRM, all the parameters are fixed, while in the Tikhonov method the regularization parameter needs to be well selected.

GPU-based Monte Carlo simulation for light propagation in complex heterogeneous tissues

As the most accurate model for simulating light propagation in heterogeneous tissues, Monte Carlo (MC) method has been widely used in the field of optical molecular imaging. However, MC method is time-consuming due to the calculations of a large number of photons propagation in tissues. The structural complexity of the heterogeneous tissues further increases the computational time. In this paper we present a parallel implementation for MC simulation of light propagation in heterogeneous tissues whose surfaces are constructed by different number of triangle meshes. On the basis of graphics processing units (GPU), the code is implemented with compute unified device architecture (CUDA) platform and optimized to reduce the access latency as much as possible by making full use of the constant memory and texture memory on GPU. We test the implementation in the homogeneous and heterogeneous mouse models with a NVIDIA GTX 260 card and a 2.40GHz Intel Xeon CPU. The experimental results demonstrate the feasibility and efficiency of the parallel MC simulation on GPU.

A tetrahedron-based inhomogeneous Monte Carlo optical simulator

Optical imaging has been widely applied in preclinical and clinical applications. Fifteen years ago, an efficient Monte Carlo program 'MCML' was developed for use with multi-layered turbid media and has gained popularity in the field of biophotonics. Currently, there is an increasingly pressing need for simulating tools more powerful than MCML in order to study light propagation phenomena in complex inhomogeneous objects, such as the mouse. Here we report a tetrahedron-based inhomogeneous Monte Carlo optical simulator (TIM-OS) to address this issue. By modeling an object as a tetrahedron-based inhomogeneous finite-element mesh, TIM-OS can determine the photon-triangle interaction recursively and rapidly. In numerical simulation, we have demonstrated the correctness and efficiency of TIM-OS.

Study on Photon Transport Problem Based on the Platform of Molecular Optical Simulation Environment

As an important molecular imaging modality, optical imaging has attracted increasing attention in the recent years. Since the physical experiment is usually complicated and expensive, research methods based on simulation platforms have obtained extensive attention. We developed a simulation platform named Molecular Optical Simulation Environment (MOSE) to simulate photon transport in both biological tissues and free space for optical imaging based on noncontact measurement. In this platform, Monte Carlo (MC) method and the hybrid radiosity-radiance theorem are used to simulate photon transport in biological tissues and free space, respectively, so both contact and noncontact measurement modes of optical imaging can be simulated properly. In addition, a parallelization strategy for MC method is employed to improve the computational efficiency. In this paper, we study the photon transport problems in both biological tissues and free space using MOSE. The results are compared with Tracepro, simplified spherical harmonics method (SP_n), and physical measurement to verify the performance of our study method on both accuracy and efficiency.

Feasibility of Monte CarlO simulations in Quantitative Tissue Imaging

Purpose

The feasibility of Monte Carlo simulations as a tool to facilitate quantitative image analysis is investigated by means of simulating light transport in skin phantoms.

Methods

A Monte Carlo tool is used to compare if simulated fluorescent signals show agreement with measured data. The lipophilic fluorescent probe Nile Red and dedicated skin phantoms are also used in simulations to investigate the influence of the optical properties of the skin on the signal.

Results

It is shown that the simulated and measured fluorescence signals show linear behavior up to a certain concentration of Nile Red. The simulations of the skin phantoms show the varying influence of single skin layers on the fluorescence signal. A calibration factor for quantitative analysis can be determined for the different skin layers.

Conclusion

Characterizing the influence of different media on imaging signals is a primary task in developing quantitative analysis methods. Monte Carlo simulations are a useful tool to investigate imaging properties of biological specimen where quantifying signals is important. However, detailed models must be provided.

Spectrally resolved bioluminescence tomography with the third-order simplified spherical harmonics approximation

Bioluminescence imaging has been extensively applied to in vivo small animal imaging. Quantitative three-dimensional bioluminescent source information obtained by using bioluminescence tomography can directly and much more accurately reflect biological changes as opposed to planar bioluminescence imaging. Preliminary simulated and experimental reconstruction results demonstrate the feasibility and promise of bioluminescence tomography. However, the use of multiple approximations, particularly the diffusion approximation theory, affects the quality of in vivo small animal-based image reconstructions. In the development of new reconstruction algorithms, high-order approximation models of the radiative transfer equation and spectrally resolved data introduce new challenges to the reconstruction algorithm and speed. In this paper, an SP(3)-based (the third-order simplified spherical harmonics approximation) spectrally resolved reconstruction algorithm is proposed. The simple linear relationship between the unknown source distribution and the spectrally resolved data is established in this algorithm. A parallel version of this algorithm is realized, making BLT reconstruction feasible for the whole body of small animals especially for fine spatial domain discretization. In simulation validations, the proposed algorithm shows improved reconstruction quality compared with diffusion approximation-based methods when high absorption, superficial sources and detection modes are considered. In addition, comparisons between fine and coarse mesh-based BLT reconstructions show the effects of numerical errors in reconstruction image quality. Finally, BLT reconstructions using in vivo mouse experiments further demonstrate the potential and effectiveness of the SP(3)-based reconstruction algorithm.

Three-dimensional bioluminescence tomography based on Bayesian approach

Bioluminescence tomography (BLT) poses a typical ill-posed inverse problem with a large number of unknowns and a relatively limited number of boundary measurements. It is indispensable to incorporate a priori information into the inverse problem formulation in order to obtain viable solutions. In the paper, Bayesian approach has been firstly suggested to incorporate multiple types of a priori information for BLT reconstruction. Meanwhile, a generalized adaptive Gaussian Markov random field (GAGMRF) prior model for unknown source density estimation is developed to further reduce the ill-posedness of BLT on the basis of finite element analysis. Then the distribution of bioluminescent source can be acquired by maximizing the log posterior probability with respect to a noise parameter and the unknown source density. Furthermore, the use of finite element method makes the algorithm appropriate for complex heterogeneous phantom. The algorithm was validated by numerical simulation of a 3-D micro-CT mouse atlas and physical phantom experiment. The reconstructed results suggest that we are able to achieve high computational efficiency and accurate localization of bioluminescent source.

A study of photon propagation in free-space based on hybrid radiosity-radiance theorem

Noncontact optical imaging has attracted increasing attention in recent years due to its significant advantages on detection sensitivity, spatial resolution, image quality and system simplicity compared with contact measurement. However, photon transport simulation in free-space is still an extremely challenging topic for the complexity of the optical system. For this purpose, this paper proposes an analytical model for photon propagation in free-space based on hybrid radiosity-radiance theorem (HRRT). It combines Lambert's cosine law and the radiance theorem to handle the influence of the complicated lens and to simplify the photon transport process in the optical system. The performance of the proposed model is evaluated and validated with numerical simulations and physical experiments. Qualitative comparison results of flux distribution at the detector are presented. In particular, error analysis demonstrates the feasibility and potential of the proposed model for simulating photon propagation in free-space.

A source reconstruction algorithm based on adaptive hp-FEM for bioluminescence tomography

As a novel modality of molecular imaging, bioluminescence tomography (BLT) is used to in vivo observe and measure the biological process at cellular and molecular level in small animals. The core issue of BLT is to determine the distribution of internal bioluminescent sources from optical measurements on external surface. In this paper, a new algorithm is presented for BLT source reconstruction based on adaptive hp-finite element method. Using adaptive mesh refinement strategy and intelligent permissible source region, we can obtain more accurate information about the location and density of sources, with the robustness, stability and efficiency improved. Numerical simulations and physical experiment were both conducted to verify the performance of the proposed algorithm, where the optical data on phantom surface were obtained via Monte Carlo simulation and CCD camera detection, respectively. The results represent the merits and potential of our algorithm for BLT source reconstruction.

Source reconstruction for spectrally-resolved bioluminescence tomography with sparse a priori information

Through restoration of the light source information in small animals in vivo, optical molecular imaging, such as fluorescence molecular tomography (FMT) and bioluminescence tomography (BLT), can depict biological and physiological changes observed using molecular probes. A priori information plays an indispensable role in tomographic reconstruction. As a type of a priori information, the sparsity characteristic of the light source has not been sufficiently considered to date. In this paper, we introduce a compressed sensing method to develop a new tomographic algorithm for spectrally-resolved bioluminescence tomography. This method uses the nature of the source sparsity to improve the reconstruction quality with a regularization implementation. Based on verification of the inverse crime, the proposed algorithm is validated with Monte Carlo-based synthetic data and the popular Tikhonov regularization method. Testing with different noise levels and single/multiple source settings at different depths demonstrates the improved performance of this algorithm. Experimental reconstruction with a mouse-shaped phantom further shows the potential of the proposed algorithm.

An anatomical mouse model for multimodal molecular imaging

In order to evaluate and improve multimodal molecular imaging technology, a three-dimensional anatomical model of a BALB/c mouse was developed based on micro-CT imaging with a liver-specific contrast agent Fenestra LC. By using image processing and interactive segmentation methods, we delineated some primary organs and tissues, including the skin, skeleton, muscle, heart, lung, liver and spleen. Finally, cone-beam x-ray CT and bioluminescence tomography simulation experiments demonstrate the availability and flexibility of the proposed mouse model.