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The neurotoxins of venomous scorpion act on ion channels. Whether these neurotoxins are retained in processed Buthus martensii Karsch scorpions used in traditional Chinese medicine materials is unknown. Comprehensive mass spectrometry-based proteomic characterization of functionally active toxins in the processed medicinal scorpion material revealed 22 full-length and 44 truncated thermostable potassium channel-modulatory toxins that preserved six conserved cysteine residues capable of forming the three disulfide bonds necessary for toxicity. Additionally, a broad spectrum of degraded toxin fragments was found, indicating their relative thermal instability which enabled toxicity reduction. Furthermore, the suppression of interleukin-2 (IL-2) production in Jurkat cells and the reduced delayed-type hypersensitivity (DTH) response demonstrated that the extracts have immunoregulatory activity both in vitro and in vivo. Our work describes the first “map” of functionally active scorpion toxins in processed scorpion medicinal material, which is helpful to unveil the pharmaceutical basis of the processed scorpion medicinal material in traditional Chinese medicine. Biological significance Scorpions have been used as medicinal materials in China for more than one thousand years. This is an example of the well-known “Combat poison with poison” strategy common to traditional Chinese medicine. In the past 30 years, extensive investigations of Chinese scorpions have indicated that the neurotoxins in the scorpion venom are the main toxic components and they target various ion channels in cell membranes. However, whether these neurotoxins are retained in processed Buthus martensii Karsch scorpions used for traditional Chinese medicine remains unknown. Our study described the thermal stability and instability of potassium channel-modulatory neurotoxins in processed scorpions and helps to understand the pharmaceutical basis underling the strategy of “combat poison with poison to cure diseases”.

In the medical imaging field, molecular imaging is a rapidly developing discipline and forms many imaging modalities, providing us effective tools to visualize, characterize, and measure molecular and cellular mechanisms in complex biological processes of living organisms, which can deepen our understanding of biology and accelerate preclinical research including cancer study and medicine discovery. Among many molecular imaging modalities, although the penetration depth of optical imaging and the approved optical probes used for clinics are limited, it has evolved considerably and has seen spectacular advances in basic biomedical research and new drug development. With the completion of human genome sequencing and the emergence of personalized medicine, the specific drug should be matched to not only the right disease but also to the right person, and optical molecular imaging should serve as a strong adjunct to develop personalized medicine by finding the optimal drug based on an individual's proteome and genome. In this process, the computational methodology and imaging system as well as the biomedical application regarding optical molecular imaging will play a crucial role. This review will focus on recent typical translational studies of optical molecular imaging for personalized medicine followed by a concise introduction. Finally, the current challenges and the future development of optical molecular imaging are given according to the understanding of the authors, and the review is then concluded.

Finding the center of rotation is an essential step for accurate three-dimensional reconstruction in optical projection tomography (OPT). Unfortunately current methods are not convenient since they require either prior scanning of a reference phantom, small structures of high intensity existing in the specimen, or active participation during the centering procedure. To solve these problems this paper proposes a fast and automatic center of rotation search method making use of parallel programming in graphics processing units (GPUs). Our method is based on a two step search approach making use only of those sections of the image with high signal to noise ratio. We have tested this method both in non-scattering ex vivo samples and in in vivo specimens with a considerable contribution of scattering such as Drosophila melanogaster pupae, recovering in all cases the center of rotation with a precision 1/4 pixel or less.

In this study, a fast and robust reconstruction method based on the separable approximation and the adaptive regularization is presented for fluorescence molecular tomography. The subproblems can be established and solved efficiently through separable approximation, and the convergence process can be also accelerated by adaptive regularization. As is well known, the regularization parameter has an important impact on the results, and finding the optimal or near-optimal regularization parameter automatically is an challenging task. To solve this problem, the regularization parameter in the proposed method is updated heuristically instead of being determined manually or empirically. This adaptive regularization strategy of the proposed method can perform accurate reconstruction almost without worrying about the choice of the regularization parameter. By contrast, improper choice of the regularization parameter may cause larger location errors for the three contrasting methods. The proposed method is proved robust and insensitive to parameters, which can improve the reconstruction accuracy. Moreover, the proposed method was about 1-2 orders of magnitude faster than the contrasting methods commonly used in fluorescence tomography reconstruction. Furthermore, reliable performance on different initial unknown values and different noise levels was also investigated. Finally, the potential of the proposed method in a practical application was further validated by the physical experiment with a mouse model. (C) 2012 Elsevier B.V. All rights reserved.

We review the current state-of-the-art of bioluminescence tomography (BLT) imaging, which is an emerging technique for monitoring and assessment of biological processes in vivo. The aim of BLT is to reconstruct 3-D distribution of the internal bioluminescent source using boundary measurements acquired by a BLT imaging system. Thus, BLT becomes a task of solving an inverse problem with an appropriate photon propagation model. In this paper, we discuss recent advances in models of photon transport, and review in detail the current techniques for BLT reconstructions. Specifically, we consider the reconstruction algorithms based on the permissible source region strategy, and multispectral and regularization techniques. The progress in the BLT imaging system is also briefly introduced. Finally, future challenges are also discussed.

Molecular imaging can provide qualitative or quantitative physiological and pathological knowledge at the cellular and molecular levels for biomedical research, which has experienced a remarkable growth in recent years. Among molecular imaging techniques, optical molecular imaging has attracted considerable attention in view of its excellent performance and high cost-effectiveness. However, heretofore the experimental objects of optical imaging technique are mainly small animals like rats and mice, and optical imaging can hardly be used for clinical research not only because of the limited tissue penetration, but also due to the scarcity of appropriate imaging probes. As a newly emerging and very promising imaging modality, Cerenkov luminescence employs light signals emitting from radionuclides used in nuclear imaging based on Cerenkov radiation, which can provide more potential optical imaging probes for clinical application and facilitate the realization of multimodality imaging. In this paper, the fundamentals of Cerenkov luminescence are first introduced, and then Cerenkov luminescence imaging and tomography as well as the corresponding biomedical applications are represented. Finally, limitations of Cerenkov luminescence and the discussion of this contribution are covered.

Bioluminescence tomography (BLT) is a molecular imaging modality which can three-dimensionally resolve the molecular processes in small animalsin vivo. The ill-posedness nature of BLT problem makes its reconstruction bears nonunique solution and is sensitive to noise. In this paper, we proposed a sparse BLT reconstruction algorithm based on semigreedy method. To reduce the ill-posedness and computational cost, the optimal permissible source region was automatically chosen by using an iterative search tree. The proposed method obtained fast and stable source reconstruction from the whole body and imposed constraint without using a regularization penalty term. Numerical simulations on a mouse atlas, andin vivomouse experiments were conducted to validate the effectiveness and potential of the method.

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.

Molecular imaging, including fluorescence imaging (FMI), bioluminescence imaging (BLI), positron emission tomography (PET), single-photon emission-computed tomography (SPECT), and computed tomography (CT), has a pivotal role in the process of tumor and relevant drug research. CT, especially Micro-CT, can provide the anatomic information for a region of interest (ROI); PET and SPECT can provide functional information for the ROI. BLI and FMI can provide optical information for an ROI. Tumor angiogenesis and relevant drug development is a lengthy, high-risk, and costly process, in which a novel drug needs about 10–15 years of testing to obtain Federal Drug Association (FDA) approval. Molecular imaging can enhance the development process by understanding the tumor mechanisms and drug activity. In this paper, we focus on tumor angiogenesis, and we review the characteristics of molecular imaging modalities and their applications in tumor angiogenesis and relevant drug research.

Katsevich reconstruction algorithm represents a breakthrough for helical cone-beam computed tomography (CT) reconstruction, because it is the first exact cone-beam reconstruction algorithm of filtered backprojection (FBP) type with 1-D shift-invariant filtering. Although FBP-type reconstruction algorithm is effective, 3-D CT reconstruction is time-consuming, and the accelerations of Katsevich algorithm on CPU or cluster have been widely studied. In this paper, Katsevich algorithm is accelerated by using graphics processing unit, including flat-detector and curved-detector geometry in the case of helical orbit. An overscan formula is derived, which helps to avoid unnecessary overscan in practical CT scanning. Based on the overscan formula, a volume-blocking method in device memory is proposed. One advantage of the blocking method is that it can reconstruct large volume with high speed.

As a molecular imaging technique, bioluminescence tomography (BLT) with its highly sensitive detection and facile operation can significantly reveal molecular and cellular information in vivo at the whole-body small animal level. However, because of complex photon transportation in biological tissue and boundary detection data with high noise, bioluminescent sources in deeper positions generally cannot be localized. In our previous work, we used achromatic or monochromatic measurements and an a priori permissible source region strategy to develop a multilevel adaptive finite-element algorithm. In this paper, we propose a spectrally solved tomographic algorithm with a posteriori permissible source region selection. Multispectral measurements, and anatomical and optical information first deal with the nonuniqueness of BLT and constrain the possible solution of source reconstruction. The use of adaptive mesh refinement and permissible source region based on a posteriori measures not only avoids the dimension disaster arising from the multispectral measured data but also reduces the ill-posedness of BLT and therefore improves the reconstruction quality. Reconsideration of the optimization method and related modifications further enhance reconstruction robustness and efficiency. We also incorporate into the method some improvements for reducing computational burdens. Finally, using a whole-body virtual mouse phantom, we demonstrate the capability of the proposed BLT algorithm to reconstruct accurately bioluminescent sources in deeper positions. In terms of optical property errors and two sources of discernment in deeper positions, this BLT algorithm represents the unique predominance for BLT reconstruction.

As a high-sensitivity imaging modality, bioluminescence tomography can reconstruct the three-dimensional (3-D) location of an internal luminescent source based on the 3-D surface light distribution. However, we can only get the multi-orientation two-dimensional (2-D) bioluminescence distribution in the experiments. Therefore, developing an accurate universal registration method is essential for following bioluminescent source reconstruction. We can then map the multi-orientation 2-D bioluminescence distribution to the 3-D surface derived from anatomical information with it. We propose a 2-D -to-3-D registration method based on iterated optimal projection and applied it in a registration and reconstruction study of three transgenic mice. Compared with traditional registration methods based on the fixed points, our method was independent of the markers and the registration accuracy of the three experiments was improved by 0.3, 0.5, and 0.4 pixels, respectively. In addition, based on the above two registration results using the two registration methods, we reconstructed the 3-D location of the inner bioluminescent source in the three transgenic mice. The reconstruction results showed that the average error distance between the center of the reconstructed element and the center of the real element were reduced by 0.32, 0.48, and 0.39 mm, respectively.

Molecular imaging emerging in the early 21st century has been rapidly developed. Over more than 10 years, optical molecular imaging has become an important tool in preclinical research and has been translated into the clinical for clinical diagnosis and therapy. Computational methodology has shown its indispensable position in developing molecular probes, building imaging instruments, researching reconstruction algorithms, and studying signal and image processing. Relevant research not only adapts existing computational methods but also develops novel strategies to tackle with challenging problems in optical molecular imaging. This special issue is devoted to the topic of computational algorithms and strategies in optical molecular imaging. Five exciting papers assembled in this special issue present a common theme, that is, improving the imaging and image processing performance in optical molecular imaging by using computational techniques. The papers address several problems in optical molecular imaging, including optical tomographic reconstruction for bioluminescence tomography and image processing such as image segmentation, 3D rendering, and cell morphological measurements. In detail, J. Yu et al. make use of a hybrid multilevel adaptive finite element reconstruction scheme and sparse regularization to acquire quantitative information of bioluminescent sources. The work of Q. Wu et al. is to take advantage of a high-order photon propagation model and Bregman optimization algorithm to improve bioluminescence tomography. The morphology information of living cells and 3D tissue images at the micrometer level can be obtained by using existing computational algorithms from the developed digital holographic microscopy and multichannel spectral imaging laser scanning confocal microscope in the research progress of Y. Wang et al. and Y. Zhang et al., respectively. J. Zheng et al. report that retinal vessels are also efficiently extracted with multiscale hessian-enhancement-based nonlocal mean filter.

Optical molecular imaging has been rapidly developed to noninvasively visualize in vivo physiological and pathological processes involved in normal and suffering organisms at the cellular and molecular levels, in which advanced optical imaging technology and modern molecular biology are being combined to provide a state-of-the-art tool for preclinical biomedical research. Among optical molecular imaging modalities, bioluminescence tomography (BLT) has experienced considerable growth and attracted much attention in recent years for its excellent performance, unique advantages, and high cost-effectiveness. This article focuses on the genesis and development of BLT, especially for its computational methodology, imaging system, and biomedical application. An overview of the advantages and challenges of the conventional planar bioluminescence imaging technique is first described in comparison with currently available molecular imaging modalities. The imaging algorithms for inverse source reconstruction are classified and summarized according to different a priori knowledge, followed by a simple depiction of the uniqueness theorems of BLT solution. Diverse imaging systems for obtaining three-dimensional quantitative information of internal bioluminescent sources are then reviewed. The latest application examples of BLT in tumor study and drug discovery are introduced and compared with other mature imaging technologies. Finally, the paper is concluded and an attractive prospect for BLT ispredicted.

Tomographic bioluminescence imaging (TBI), with visible light emission in living organisms, is an effective way of molecular imaging, which allows for the study of ongoing tumor biological processes in vivo and non-invasively. This newly developed technology enables three-dimensional accuracy localization and quantitative analysis of the target tumor cells in small animal via reconstructing the images acquired by the high-resolution imaging system. Due to the difficulty of reconstruction, which is often referred to an ill-posed inverse problem, continuous efforts are still made to find more practical and efficient approaches. In this paper, an iteratively re-weighted minimization (IRM) has been applied to reconstruct the entire source distribution, which is known as sparse signals, inside the target tissue with the limited outgoing photon density on its boundary. By introducing a weight function into the objective function, we convert the l p norm problem into a more simple form of l 2 norm to reduce the computational complexity. The weight function is updated in each iterative step to compute the final optimal solution more efficiently. This method is proved to be robust to different parameters, and mouse experiments are conducted to validate the feasibility of IRM approach, which is also reliable at whole-body imaging.

Fluorescence molecular tomography (FMT) can three-dimensionally resolve molecular activities in in vivo small animal through the reconstruction of the distribution of fluorescent probes. Due to large number of unknowns and limited measurements from the surfaces of small animals, the FMT problem is often ill-posed and ill-conditioned. Though various L2-norm regularizations can make the solution stable, they usually make the solution over-smoothed. During the early stages of tumor detection, fluorescent sources that indicate the distribution of tumors are usually small and sparse, which can be regarded as a type of a priori information. L1-norm regularizations have been incorporated to promote the sparsity of optical tomographic problems. In this paper, an efficient method with the L1-norm regularization based on coordinate descent is proposed to solve the FMT problem with extremely limited measurements. The proposed method minimizes the objective by solving a sequence of scalar minimization subproblems in multi-variable minimization. Each subproblem improves the estimate of the solution via minimizing along a determined coordinate with all other coordinates fixed. This algorithm first updates the coordinate that makes the energy decrease the most. Non-existence of matrix-vector multiplication in the iteration process makes the proposed algorithm time-efficient. To evaluate this method, we compare it to the iterated-shrinkage-based algorithm with L1-norm regularization in numerical experiments. The proposed algorithm is able to obtain satisfactory reconstruction results even when the measurements are very limited. Besides, the proposed algorithm is about two orders of magnitude faster than the iterated-shrinkage-based algorithm, which enables the proposed algorithm into practical applications.

Photoacoustic imaging is a noninvasive biomedical imaging modality for visualizing the internal structure and function of soft tissues. In this paper we present a combined iterative reconstruction algorithm to recover the distribution of optical absorption coefficients. Such a combination of the simultaneous algebraic reconstruction technique (SART) algorithm and the filtered back projection (FBP) algorithm bears the promise to improve the image quality of fast but not accurate. The different between the present proposal and traditional ART algorithm is that we computer a full error image with FBP, which is used to projection differences between original and reconstruction image, and to update the solution in each iteration step. The combined iterative reconstruction algorithm works well in rectification of the measurement and converges quickly onto an accurate estimate of the distribution of absolute absorption coefficients. Numerical simulations demonstrate that the proposed algorithm effectively reduces the artifacts and blurs, and yields better quality of reconstruction image than that with FBP algorithm in terms of both accuracy and robustness to noise.

Solution with adjustable sparsity to tomographic imaging of Cerenkov photons is presented in this work. The sparsity of radionuclides' distribution in tissues is an objective but unknown fact, and the inverse model of qualitative data is an ill-posed problem. Based on the optimization technique, the uniqueness of numerical solution to the ill-conditioned compact operator can be guaranteed by use of sparse regularization with the approximate message-passing (AMP) method. After absorbing formulations with the AMP, we analyzed the behavior of the hard thresholding operator. Iteratively numerical solutions were used to approximate the real light source by assuming the number of non-zero solution in manual mode. This modified AMP algorithm was performed in numerical simulation and physical experiments with 2-[18F]fluoro-2-deoxy-D-glucose. Experimental results indicated that the proposed method was a kind of low-complexity iterative thresholding algorithms for reconstructing 3D sparse distribution from a small set of optical measurements.

Bioluminescence imaging (BLI) is an optical molecular imaging modality for monitoring physiological and pathological activities at the molecular level. The information of bioluminescent probe distribution in small animals can be threedimensionally and quantitatively obtained by bioluminescence tomography (BLT). Due to ill-posed nature, BLT may bear multiple solutions and aberrant reconstruction in the presence of measurement noise and optical parameter mismatches. Among different regularization methods, L2-type regularization strategy is the most popular and commonly-applied method, which minimizes the output-least-square formulation incorporated with the l2-norm regularization term to stabilize the problem. However, it often imposes over-smoothing on the reconstruction results. In contrast, for many practical applications, such as early detection of tumors, the volumes of the bioluminescent sources are very small compared with the whole body. In this paper, L1 regularization is used to fully take advantage of the sparsity prior knowledge and improve both efficiency and stability. And then a reconstruction method based on the augmented Lagrangian approach is proposed, which considers the BLT problem as the constrained optimization problem and employs the Bregman iterative method to deal with it. By using "divide and conquer" approach, the optimization problem can be exactly and fast solved by iteratively solving a sequence of unconstrained subproblems. To evaluate the performance of the proposed method in turbid mouse geometry, stimulate experiments with a heterogeneous 3D mouse atlas are conducted. In addition, physical experiments further demonstrate the potential of the proposed algorithm in practical applications.

Imaging modality of radionuclides has been enriched by an optical approach, Cerenkov luminescence tomography (CLT). Referred to the traditional radionuclide imaging, such as positron emission tomography (PET) or single photon emission computed tomography (SPECT), any incremental improvement of CLT imaging is consistent with the application to information needs. In this contribution, the paper presents an l(1)-regularized imaging method for CLT problem. After utilizing the Vavilov-Cerenkov effect via third-order simplified spherical harmonics (SP(3)) approximation, we establish the large-scale linear equations in the CLT framework. The derived linear problem is seriously ill-posed, and transformed into an l(1)-regularized least squares program. The inverse solution to these equations is the three-dimensional radioisotope recovery data by an interior-point method. In the physical phantom and the in vivo mouse experiment, results demonstrate that the proposed technique produces better imaging quality and improves the reconstruction efficacy, compared with those from diffusion approximation with the Tikhonov regularization.

In this paper, we explore the application of total variation regularization method for bioluminescence tomography (BLT) with an adaptive regularization parameter choice approach. Since BLT is a seriously ill-posed problem, therefore, l(2) regularized methods are frequently adopted to recover the bi-oluminescent sources. However, l(2) regularized methods typically lead to smooth reconstructions. In this paper, we investigated the use of total variation (TV) regularization to improve the quality of BLT reconstruction. Furthermore, the regularization parameter in TV method was chosen adaptively to make the proposed algorithm more stable. Results on simulation data provide evidence that the reconstructed source can be localized accurately compared with l(2) method. Meanwhile, the effectiveness of utility of the parameter choice were illustrated. Finally, different levels of noisy data were added to validate the performance of the proposed algorithm.

Imaging modality of radionuclides has been enriched by an optical approach, Cerenkov luminescence tomography (CLT). Referred to the traditional radionuclide imaging, such as positron emission tomography (PET) or single photon emission computed tomography (SPECT), any incremental improvement of CLT imaging is consistent with the application to information needs. In this contribution, the paper presents an l 1 -regularized imaging method for CLT problem. After utilizing the Vavilov-Cerenkov effect via third-order simplified spherical harmonics (SP 3 ) approximation, we establish the large-scale linear equations in the CLT framework. The derived linear problem is seriously ill-posed, and transformed into an l 1 -regularized least squares program. The inverse solution to these equations is the three-dimensional radioisotope recovery data by an interior-point method. In the physical phantom and the in vivo mouse experiment, results demonstrate that the proposed technique produces better imaging quality and improves the reconstruction efficacy, compared with those from diffusion approximation with the Tikhonov regularization.

Development of more tumor-specific radiopharmaceuticals is not enough; to understand the disease, we must study data modeling. Although fluoro-18-deoxyglucose positron emission tomography can map a multi-peak distribution of trace radioisotopes, optical tomography should also be able to redirect the distribution.Multi-view image acquisition of small animals injected with 2-deoxy-2-[(18)F]fluoro-D: -glucose began with X-ray computed tomography scanning and Cerenkov luminescence imaging. After fusion processing, utilization of the geometric row scaling and L (1/2) regularization operator effectively generates in vivo Cerenkov luminescence tomography images with the SP(3) forward model.The identification is confirmed by the comparison between tumor-specific tomography from Cerenkov emission and the radioactivity measured in vitro.The proposed technique can quickly localize the mobility of radionuclides and uptake by organs, which provides an imaging methodology in oncology.

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.

Applying Cerenkov luminescence tomography (CLT) to localizing Cerenkov light sources in situ is still in its nascent stage. One of the obstacles hindering the development of the CLT is the lack of dedicated imaging mode. In this contribution, the paper presented a Cerenkov optical imaging mode, in which the propagation of optical photons inside tissues generated by the Vavilov-Cerenkov effect is modeled based on simplified spherical harmonics approximation. As a significantly more transport-like and computational-efficient approximation theory, the performance of the third-order simplified spherical harmonics approximation ( SP 3 ) in the CLT forward is investigated in stages. Finally, the performance of the proposed forward model is validated using numerical phantoms and compared with the simulation data based on the Monte Carlo method.

Fluorescence molecular tomography (FMT) has become a promising imaging modality for in vivo small animal molecular imaging, and has many successful applications. This is partly due to the wealth of the fluorescent probes. By labeling the regions of interest with fluorescent probes, FMT can achieve non-invasive investigation of the biological process by localizing the targeted probes based on certain inverse mathematical models. However, FMT is usually an illposed problem, and some form of regularization should be included to stabilize the problem, which can be considered as the a priori information of the fluorescent probe bio-distribution. When FMT is used for the early detection of tumors, an important characteristic is the sparsity of the fluorescent sources. This is because tumors are usually very small and sparse at this stage. Considering this, general sparsity-promoting Lp-norm regularization is utilized in this paper. The iterated shrinkage based reconstruction method is adopted to solve the general Lp regularization problem. However, the original iterated shrinkage method is proved to have a linear convergence rate, and a large number of iterations are needed to obtain satisfactory results. In this paper, an improved iterated shrinkage based FMT reconstruction algorithm is proposed. By using the solutions from two previous iterations to determine the current solution, the convergence rate can be greatly increased. Heterogeneous simulation experiment shows that the proposed method can obtain comparable results with greatly reduced number of iterations compared with the original iterated shrinkage based method, which makes it a practical reconstruction algorithm.

In vivo bioluminescence imaging (BLI) has played a more and more important role in biomedical research ofsmall animals. Tomographic bioluminescence imaging (TBI) further translates the BLI optical information intothree-dimensional bioluminescent source distribution, which could greatly facilitate applications in related stud-ies. Although the diu sion approximation (DA) is one of the most widely-used forward models, higher-orderapproximations are still needed for in vivo small animal imaging. In this work, as a relatively accurate andhigher-order approximation theory, a simpli“ed spherical harmonics approximation (SP N ) is applied for hetero-geneous tomographic bioluminescence imaging in vivo . Furthermore, coupled with the SP N , a generalized graphcuts optimization approach is utilized, making BLT reconstructions fast and suit for the whole body of smallanimals. Heterogeneous in vivo experimental reconstructions via the higher-order approximation model demon-strate higher tomographic imaging quality, which is shown the capability for practical biomedical tomographicimaging applications.Keywords: optical imaging, higher-order approximation, tomographic bioluminescence imaging, inverse prob-lem

Bioluminescence tomography (BLT) is a novel optical molecular imaging (MI) modality. It can reconstruct the inner bioluminescent light source distribution, according to the surface light distribution. The trust region method (TRM) can overcome the ill-posedness of BLT for its regularization property. As there exists a "TRUST" function that can solve the trust region subproblem in Matlab and Matlab's powerful matrix operation ability suited for TRM, the TRM is implemented in Matlab. Then the Matlab code of TRM is transformed into a dynamic linked library (DDL) and mixed together with the C++ code of the adaptive finite element (AFE) framework, using the mixed-language programming technology (MLPT). There are two main advantages of the MLPT. The first is taking advantages of all the participated programming languages. The second is time efficient. The usual way of transferring data between programmes written in different programming languages is to write the data first into files that are stored in the hard discs in one programme, and then read the files from another programme. Besides wasting time on writing and reading, it is difficult to keep the precision of the data. The DLL based MLPT can eliminate the need of installing code compilers in the platform running the software. Furthermore, in DLL, the code is implemented in C/C++ with high time efficiency, while the code in Matlab remains relatively low time efficiency. Finally, a numerical experiment is carried out to show MLPT's usage in the source reconstruction procedure of BLT, using the MLPT based on DLL.

Bioluminescence imaging (BLI) offers an alternative opportunity for non-invasively visualizing biological processes at the physiological and molecular levels in whole animals. Tomographic bioluminescence imaging (TBI) can further translate planar imaging into three-dimensional quantitative bioluminescent source distribution. Although many reconstruction methods have been developed, efforts are still needed to make the tomographic methods more practical for biomedical imaging experiments. In this paper, a fast reconstruction algorithm based on parallel coordinate descent optimization is proposed for in vivo tomographic bioluminescence imaging. Based on the shrinking operation, the proposed method is very efficient. Moreover, in order to obtain the analytic form of the updating shrinkage function, a smooth and convex approximation to the regularization term L p is utilized, so this term can be easily generalized for 1 ≤ p ≤ 2. In the experiments, the imaging performance of the proposed method demonstrates that it has the ability to recover reliable tomographic results with a wide range of regularization parameters and different p. Moreover, the high efficiency makes it potential for real time imaging reconstruction.

Only a planar bioluminescence image acquired from an ordinary cooled charge-coupled device (CCD) array every time, how to re-establish the three-dimensional small animal shape and light intensity distribution on the surface has become urgent to be solved as a bottleneck of bioluminescence tomography (BLT) reconstruction. In this paper, a finite element algorithm to solve the Dirichlet type problem for the first order Hamilton-Jacobi equation related to the shape-fromshading model is adopted. The algorithm outputting the globally maximal solution of the above problem avoids cumbersome boundary conditions on the interfaces between light and shadows and the use of additional information on the surface. The results of the optimization method are satisfied. It demonstrates the feasibility and potential of the finite element shape-fromshading (FE-SFS) model for reconstructing the small animal surface that lays one of key foundations for a fast low-cost application of the BLT in the next future.

Generation of an accurate Cerenkov luminescence imaging model is a current issue of nuclear tomography with optical techniques. The article takes a pro-active approach toward whole-body Cerenkov luminescence tomography. The finite element framework employs the equation of radiative transfer via the third-order simplified spherical harmonics approximation to model Cerenkov photon propagation in a small animal. After this forward model is performed on a digital mouse with optical property heterogeneity and compared with the Monte Carlo method, we investigated the whole body reconstruction algorithm along a regularization path via coordinate descent. The endpoint of the follow-up study is the in vivo application, which provides three-dimensional biodistribution of the radiotracer uptake in the mouse from measured partial boundary currents. The combination of the forward and inverse model with elastic-net penalties is not only validated by numerical simulation, but it also effectively demonstrates in vivo imaging in small animals. Our exact reconstruction method enables optical molecular imaging to best utilize Cerenkov radiation emission from the decay of medical isotopes in tissues.

Through the reconstruction of the fluorescent probe distributions, fluorescence molecular tomography (FMT) can three-dimensionally resolve the molecular processes in small animals in vivo. In this paper, we propose an FMT reconstruction algorithm based on the iterated shrinkage method. By incorporating a surrogate function, the original optimization problem can be decoupled, which enables us to use the general sparsity regularization. Due to the sparsity characteristic of the fluorescent sources, the performance of this method can be greatly enhanced, which leads to a fast reconstruction algorithm. Numerical simulations and physical experiments were conducted. Compared to Newton method with Tikhonov regularization, the iterated shrinkage based algorithm can obtain more accurate results, even with very limited measurement data.