Search

Objective: Stimulated Raman projection tomography (SRPT), a recently developed label-free volumetric chemical imaging technology, has been reported to quantitatively reconstruct the distribution of chemicals in a three-dimensional (3D) complex system. The current image reconstruction scheme used in SRPT is based on a filtered back projection (FBP) algorithm that requires at least 180 angular-dependent projections to rebuild a reasonable SRPT image, resulting in a long total acquisition time. This is a big limitation for longitudinal studies on live systems. Methods: We present a sparse-view data-based sparse reconstruction scheme, in which sparsely sampled projections at 180 degrees were used to reconstruct the volumetric information. In the scheme, the simultaneous algebra reconstruction technique (SART), combined with total variation regularization, was used for iterative reconstruction. To better describe the projection process, a pixel vertex driven model (PVDM) was developed to act as projectors, whose performance was compared with those of the distance driven model (DDM). Results: We evaluated our scheme with numerical simulations and validated it for SRPT by mapping lipid contents in adipose cells. Simulation results showed that the PVDM performed better than the DDM in the case of using sparse-view data. Our scheme could maintain the quality of the reconstructed images even when the projection number was reduced to 15. The cell-based experimental results demonstrated that the proposed scheme can improve the imaging speed of the current FBP-based SRPT scheme by a factor of 9–12 without sacrificing discernible imaging details. Conclusion: Our proposed scheme significantly reduces the total acquisition time required for SRPT at a speed of one order of magnitude faster than the currently used scheme. This significant improvement in imaging speed would potentially promote the applicability of SRPT for imaging living organisms.

As an emerging volumetric imaging technique, Stimulated Raman projection tomography (SRPT) can provide quantitative distribution of chemical components in a three-dimensional (3D) volume, with a label-free manner. Currently, the filtered back-projection (FBP) algorithm is used to reconstruct the 3D volume in SRPT. However, to obtain a satisfactory reconstruction result, the FBP algorithm requires a certain amount of projection data, usually, at least 180 projections in a half circle. This leads to a long data acquisition time and hence limits dynamic and longitudinal observation of living systems. Iterative reconstruction from sparsely sampled data may reduce the total data acquisition time by reducing the projections used in the reconstruction. In this work, two total variation regularization based iterative reconstruction algorithms were selected and used in SRPT, including the simultaneous algebra reconstruction technique (SART) and the two-step iterative shrinkage/thresholding algorithm (TwIST). The well-known distance-driven model was utilized as the forward and back-projectors. We evaluated these two algorithms with numerical simulations. Using the original image as the reference, we calculated the quality of the reconstructed images. Simulation results showed that both the SART and TwIST performed better than the FBP algorithm, with larger values of the structural similarity (SSIM). Furthermore, the number of projection images can be largely reduced when the iterative reconstruction algorithm was used. Especially when the SART was used, the projection number can be reduced to 15, providing a satisfactory reconstruction image (SSIM is larger than 0.9).

Background Radionuclide-excited luminescence imaging is an optical radionuclide imaging strategy to reveal the distributions of radioluminescent nanophosphors (RLNPs) inside small animals, which uses radioluminescence emitted from RLNPs when excited by high energy rays such as gamma rays generated during the decay of radiotracers used in clinical nuclear medicine imaging. Currently, there is no report of tomographic imaging based on radioluminescence. Methods In this paper, we proposed a gamma rays excited radioluminescence tomography (GRLT) to reveal three-dimensional distributions of RLNPs inside a small animal using radioluminescence through image reconstruction from surface measurements of radioluminescent photons using an inverse algorithm. The diffusion equation was employed to model propagations of radioluminescent photons in biological tissues with highly scattering and low absorption characteristics. Results Phantom and artificial source-implanted mouse model experiments were employed to test the feasibility of GRLT, and the results demonstrated that the ability of GRLT to reveal the distribution of RLNPs such as Gd2O2S:Tb using the radioluminescent signals when excited by gamma rays produced from 99mTc. Conclusions With the emerging of targeted RLNPs, GRLT can provide new possibilities for in vivo and noninvasive examination of biological processes at cellular levels. Especially, combining with Cerenkov luminescence imaging, GRLT can achieve dual molecular information of RLNPs and nuclides using single optical imaging technology.

Background Implicit shape-based reconstruction method in fluorescence molecular tomography (FMT) is capable of achieving higher image clarity than image-based reconstruction method. However, the implicit shape method suffers from a low convergence speed and performs unstably due to the utilization of gradient-based optimization methods. Moreover, the implicit shape method requires priori information about the number of targets. Methods A shape-based reconstruction scheme of FMT with a cosinoidal level set method is proposed in this paper. The Heaviside function in the classical implicit shape method is replaced with a cosine function, and then the reconstruction can be accomplished with the Levenberg–Marquardt method rather than gradient-based methods. As a result, the priori information about the number of targets is not required anymore and the choice of step length is avoided. Results Numerical simulations and phantom experiments were carried out to validate the proposed method. Results of the proposed method show higher contrast to noise ratios and Pearson correlations than the implicit shape method and image-based reconstruction method. Moreover, the number of iterations required in the proposed method is much less than the implicit shape method. Conclusions The proposed method performs more stably, provides a faster convergence speed than the implicit shape method, and achieves higher image clarity than the image-based reconstruction method.

For a rotatory dual-head positron emission tomography (PET) system, how to determine the rotation increments is an open problem. In this study, we simulated the characteristics of a rotatory dual-head PET system. The influences of different rotation increments were compared and analyzed. Based on this simulation, the imaging performance of a prototype system was verified. A reconstruction flowchart was proposed based on a precalculated system response matrix (SRM). The SRM made the relationships between the voxels and lines of response (LORs) fixed; therefore, we added the interpolation method into the flowchart. Five metrics, including spatial resolution, normalized mean squared error (NMSE), peak signal-to-noise ratio (PSNR), contrast-to-noise (CNR), and structure similarity (SSIM), were applied to assess the reconstructed image quality. The results indicated that the 60° rotation increments with the bilinear interpolation had advantages in resolution, PSNR, NMSE, and SSIM. In terms of CNR, the 90° rotation increments were better than other increments. In addition, the reconstructed images of 90° rotation increments were also flatter than that of 60° increments. Therefore, both the 60° and 90° rotation increments could be used in the real experiments, and which one to choose may depend on the application requirement.

Fluorescence imaging is a widely used in vivo optical imaging technique for preclinical studies, but strong tissue autofluorescence and external excitation light make it suffer from a low signal-to-noise ratio (SNR). Recently, a new optical imaging method using persistent luminescence has become of interest due to its advantage of circumvention of autofluorescence and bleed-through of excitation light during signal acquisition. In this work, we proposed a tomographic imaging method based on persistent luminescence named persistent luminescence tomography (PLT), which can obtain three dimensional distributions of persistent luminescence probes deep inside small animals. Persistent luminescence signals can last several hours after excitation, which makes it possible for PLT to collect signals without interference by autofluorescence and bleed-through of excitation light, and then to reconstruct tomographic images of high quality. Phantom and mouse experiments are implemented to verify the feasiblity of PLT.

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.

For early detection and targeted therapy, receptor expression profiling is instrumental to classifying breast cancer into sub-groups. In particular, human epidermal growth factor receptor 2 (HER2) expression has been shown to have both prognostic and predictive values. Recently, an increasingly more complex view of HER2 in breast cancer has emerged from genome sequencing that highlights the role of inter- and intra-tumor heterogeneity in therapy resistance. Studies on such heterogeneity demand high-content, high-resolution functional and molecular imaging in vivo, which cannot be achieved using any single imaging tool. Clearly, there is a critical need to develop a multimodality approach for breast cancer imaging. Since 2006, grating-based x-ray imaging has been developed for much-improved x-ray images. In 2014, the demonstration of fluorescence molecular tomography (FMT) guided by x-ray grating-based micro-CT was reported with encouraging results and major drawbacks. In this paper, we propose to integrate grating-based x-ray tomography (GXT) and high-dimensional optical tomography (HOT) into the first-of-its-kind truly-fused GXT-HOT (pronounced as “Get Hot”) system for imaging of breast tumor heterogeneity, HER2 expression and dimerization, and therapeutic response. The primary innovation lies in developing a brand-new high-content, high-throughput x-ray optical imager based on several contemporary techniques to have MRI-type soft tissue contrast, PET-like sensitivity and specificity, and micro-CT-equivalent resolution. This system consists of two orthogonal x-ray Talbot-Lau interferometric imaging chains and a hyperspectral time-resolved single-pixel optical imager. Both the system design and pilot results will be reported in this paper, along with relevant issues under further investigation.

Bioluminescence tomography (BLT) has been a valuable optical molecular imaging technique to non-invasively depict the cellular and molecular processes in living animals with high sensitivity and specificity. Due to the inherent ill-posedness of BLT, a priori information of anatomical structure is usually incorporated into the reconstruction. The structural information is usually provided by computed tomography (CT) or magnetic resonance imaging (MRI). In order to obtain better quantitative results, BLT reconstruction with heterogeneous tissues needs to segment the internal organs and discretize them into meshes with the finite element method (FEM). It is time-consuming and difficult to handle the segmentation and discretization problems. In this paper, we present a fast reconstruction method for BLT based on multi-atlas registration and adaptive voxel discretization to relieve the complicated data processing procedure involved in the hybrid BLT/CT system. A multi-atlas registration method is first adopted to estimate the internal organ distribution of the imaged animal. Then, the animal volume is adaptively discretized into hexahedral voxels, which are fed into FEM for the following BLT reconstruction. The proposed method is validated in both numerical simulation and an in vivo study. The results demonstrate that the proposed method can reconstruct the bioluminescence source efficiently with satisfactory accuracy.

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.

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.

A new technique termed Helical Optical Projection Tomography (hOPT) has been developed with the aim to overcome some of the limitations of current 3D optical imaging techniques. hOPT is based on Optical Projection Tomography (OPT) with the major difference that there is a translation of the sample in the vertical direction during the image acquisition process, requiring a new approach to image reconstruction. Contrary to OPT, hOPT makes possible to obtain 3D-optical images of intact long samples without imposing limits on the sample length. This has been tested using hOPT to image long murine tissue samples such as spinal cords and large intestines. Moreover, 3D-reconstructed images of the colon of DSS-treated mice, a model for Inflammatory Bowel Disease, allowed the identification of the structural alterations. Finally, the geometry of the hOPT device facilitates the addition of a Selective Plane Illumination Microscopy (SPIM) arm, providing the possibility of delivering high resolution images of selected areas together with complete volumetric information This work was partially supported by EC FP7 collaborative grant FMT-XCT and the Bill and Melinda Gates foundation. A.A. wishes to acknowledge support from Marie Curie IEF-2010-275137. J.R. wishes to acknowledge support from EC FP7 IMI project PREDICT-TB, and the EC FP7 CIG grant HIGH-THROUGHPUT TOMO. D.D., S.Z. and J.T. acknowledge support from the National Basic Research Program of China (973 Program) under Grant 2011CB707700, the Fellowship for Young International Scientists of the Chinese Academy of Sciences under Grant 2010Y2GA03, the National Natural Science Foundation of China under Grant 81101084 and Instrument Developing Project of the Chinese Academy of Sciences under Grant No. YZ201164 Publicado

Fluorescence molecular tomography (FMT) is an important imaging technique of optical imaging. The major challenge of the reconstruction method for FMT is the ill-posed and underdetermined nature of the inverse problem. In past years, various regularization methods have been employed for fluorescence target reconstruction. A comparative study between the reconstruction algorithms based on l(1)-norm and l(2)-norm for two imaging models of FMT is presented. The first imaging model is adopted by most researchers, where the fluorescent target is of small size to mimic small tissue with fluorescent substance, as demonstrated by the early detection of a tumor. The second model is the reconstruction of distribution of the fluorescent substance in organs, which is essential to drug pharmacokinetics. Apart from numerical experiments, in vivo experiments were conducted on a dual-modality FMT/micro-computed tomography imaging system. The experimental results indicated that l(1)-norm regularization is more suitable for reconstructing the small fluorescent target, while l(2)-norm regularization performs better for the reconstruction of the distribution of fluorescent substance. (C) 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)

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.

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.

We present a method for mapping the two-dimensional (2D) bioluminescent images (BLIs) onto a three-dimensional (3D) body surface derived from the computed tomography (CT) volume data. This mapping includes two closely-related steps, the spatial registration of the 2D BLIs into the coordinate system of the CT volume data and the light flux recovering on the body surface from BLIs. By labeling markers on the body surface, we proposed an effective registration method to achieve the spatial position alignment. The subsequent light flux recovering is presented based on the inverse process of the free-space light transport model and taking the influence of the camera lens diaphragm into account. Incorporating the mapping procedure into the bioluminescence tomography (BLT) reconstruction, we developed a dual-modality BLT and CT imaging framework to provide both optical and anatomical information. The accuracy of the registration and the light flux recovering methods were evaluated via physical phantom experiments. The registration method was found to have a mean error of 0.41 mm and 0.35 mm in horizontal and vertical direction, and the accuracy of the light flux recovering method was below 5%. Furthermore, we evaluated the performance of the dual-modality BLT/CT imaging framework using a mouse phantom. Preliminary results revealed the potential and feasibility of the dual-modality imaging framework.

In in vivo optical projection tomography (OPT), object motion will significantly reduce the quality and resolution of the reconstructed image. Based on the well-known Helgason-Ludwig consistency condition (HLCC), we propose a novel method for motion correction in OPT under parallel beam illumination. The method estimates object motion from projection data directly and does not require any other additional information, which results in a straightforward implementation. We decompose object movement into translation and rotation, and discuss how to correct for both translation and general motion simultaneously. Since finding the center of rotation accurately is critical in OPT, we also point out that the system's geometrical offset can be considered as object translation and therefore also calibrated through the translation estimation method. In order to verify the algorithm effectiveness, both simulated and in vivo OPT experiments are performed. Our results demonstrate that the proposed approach is capable of decreasing movement artifacts significantly thus providing high quality reconstructed images in the presence of object motion.

As one of molecular imaging, bioluminescence tomography (BLT) aims to recover internal source from surface measurement. Being an ill-posed inverse problem, BLT source reconstruction is usually converted to an optimization problem through regularization. In this contribution, we build a bimodal hybrid imaging system consisting of BLT and micro-CT, and then propose an improved source reconstruction method based on adjoint diffusion equations (ADEs). Compared with conventional methods based on constrained minimization problem (CMP), ADEs-based method replaces expensive iterative computation with solving a group of linear ADEs. Given surface flux density, internal source power density and photon fluence rate can be efficiently determined in one step. Both numerical and physical experiments are performed to evaluate the bimodal BLT/micro-CT imaging system and this novel reconstruction method. The relevant results demonstrate the feasibility and potential of this source reconstruction method.

Gastric cancer is the second cause of cancer-related death in the world, and it remains difficult to cure because it has been in late-stage once that is found. Early gastric cancer detection becomes an effective approach to decrease the gastric cancer mortality. Bioluminescence tomography (BLT) has been applied to detect early liver cancer and prostate cancer metastasis. However, the gastric cancer commonly originates from the gastric mucosa and grows outwards. The bioluminescent light will pass through a non-scattering region constructed by gastric pouch when it transports in tissues. Thus, the current BLT reconstruction algorithms based on the approximation model of radiative transfer equation are not optimal to handle this problem. To address the gastric cancer specific problem, this paper presents a novel reconstruction algorithm that uses a hybrid light transport model to describe the bioluminescent light propagation in tissues. The radiosity theory integrated with the diffusion equation to form the hybrid light transport model is utilized to describe light propagation in the non-scattering region. After the finite element discretization, the hybrid light transport model is converted into a minimization problem which fuses an l1 norm based regularization term to reveal the sparsity of bioluminescent source distribution. The performance of the reconstruction algorithm is first demonstrated with a digital mouse based simulation with the reconstruction error less than 1mm. An in situ gastric cancer-bearing nude mouse based experiment is then conducted. The primary result reveals the ability of the novel BLT reconstruction algorithm in early gastric cancer detection.

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.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.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.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.

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.

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.

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.

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.

Bioluminescence imaging (BLI) makes it possible to elucidate molecular and cellular signatures to better understand the effects of human disease in small animal models in vivo. The unambiguous three-dimensional bioluminescent source information obtained by bioluminescence tomography (BLT) could further facilitate its applications in biomedicine. However, to the best of our knowledge, the existing gradient-type reconstruction methods in BLT are inefficient, and often require a relatively small volume of interest (VOI) for feasible results. In this paper, a fast generalized graph cuts based reconstruction method for BLT is presented, which is to localize the bioluminescent source in heterogeneous mouse tissues via max-flow/min-cut algorithm. Since the original graph cuts theory can only handle graph-representable problem, the quadratic pseudo-boolean optimization is incorporated to make the graph representable and tractable, which is called generalized graph cuts (GGC). The internal light source can be reconstructed from the whole domain, so a priori knowledge of VOI can be avoided in this method. In the simulation validations, the proposed method was validated in a heterogeneous mouse atlas, and the source can be localized reliably and efficiently by GGC; and compared with gradient-type method, the proposed method is about 25-50 times faster. Moreover, the experiments for sensitivity to the measurement errors of tissue optical properties demonstrate that, the reconstruction quality is not much affected by mismatch of parameters. In what follows, in vivo mouse BLT reconstructions further demonstrated the potential and effectiveness of the generalized graph cut based reconstruction method.

Multispectral bioluminescence tomography is becoming a promising tool because it can resolve the biodistibutionof bioluminescent reporters associated with cellular and subcellular function through several millimeters withto centimeters of tissues in vivo . Generally, to recover the bioluminescen t sources, the sour ce reconstructionproblem is formulated as a nonlinear least-squares-type bounds constrained optimization problem. However,bioluminescence tomography (BLT) is an ill-posed problem. For the sake of stability and uniqueness of BLT,many algorithmshave been proposedto regularize the problem, suchas L 2 norm and L 1 norm. Here, we proposeda new regularization method with Huber function to regularize BLT problem to obtain robustness like L 1 andrapid convergence of L 2 . Furthermore, the computational burden is largely increased with the use of spectraldata. Therefore, there is a critical need to develop a fast reconstruction algorithm for solving multispectralbioluminescence tomography. In the paper, a limited memory quasi-Newton algorithm for solving the large-scaleoptimization problem is proposed to fast localize the bioluminescent source. In the numerical simulation, aheterogeneous phantom was used to evaluate the performance of the proposed algorithm with the Monte Carlobased synthetic data. Additionally, the real mouse experiments were conducted to further evaluate the proposedalgorithm. The results demonstrate the potential and merits of the proposed algorithm.Keywords: Multispectral bioluminescence tomography (BLT ), Huber regularization, Limited-memory-BFGS-based iterative algorithm, light source reconstruction, diusion approximation, Monte Carlo methods

Bioluminescence imaging (BLI) and bioluminescence tomography (BLT) make it possible to elucidate cellular signatures to better understand the effects of human disease in small animal in vivo. However, to the best of our knowledge, the existing gradient-type reconstruction methods in BLT are not very efficient, and often require a relatively small volume of interest (VOI) for feasible results. In this paper, a fast graph cuts based reconstruction method for BLT is presented, which is to localize the bioluminescent source in heterogeneous mouse atlas via max-flow/min-cut algorithm. Since the original graph cuts theory can only handle graph-representable problem, the quadratic pseudo-boolean optimization is incorporated to make the graph tractable. The internal light source can be reconstructed from the whole domain, so a priori knowledge of VOI can be avoided in this method. In the experiments, the proposed method is validated in a heterogeneous mouse atlas, and the source can be localized reliably and efficiently by graph cuts; and compared with a gradient-type method, graph cuts is about 25–50 times faster.

Bioluminescence tomography (BLT) has become a powerful tool for whole-body small animal imaging. In this contribution, an adaptive improved element free Galerkin method (IEFGM) is presented to perform a quantitative reconstruction of the internal light source using quasi- or multi-spectral information, which not only can avoid the time-consuming mesh generation but also can reduce the ill-posedness of BLT effectively. In the algorithm, the reconstruction can be largely enhanced by an adaptive technology based on a posteriori error estimation. Finally, the numerical and physical phantom experiment results show that the bioluminescent source can be recovered accurately.

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.

As a novel optical molecular imaging technology, bioluminescence tomography (BLT) has become a research focus which can localize and quantify an internal light source with the bioluminescent signal on the external surface of a small animal. However, BLT is ill-posed because of complex photon propagation in the biological tissue and noisy boundary measured data. Moreover, the disadvantage of the finite element method (FEM) commonly used in BLT is emerged gradually due to the troublesome mesh discretization. Therefore, an adaptive meshless method is presented to perform a quantitative reconstruction of the bioluminescent source for spectrally resolved BLT in this contribution, which not only can reduce the ill-posedness of BLT effectively through the application of multi-spectral information but also can avoid the time-consuming mesh generation. In addition, the reconstruction can be largely improved using adaptive technology based on a posteriori error estimation. Finally, the performance of this method is evaluated by numerical simulation experiments.

Bioluminescence tomography (BLT) is a novel in vivo technique in small animal studies, which can reveal the molecular and cellular information at the whole-body small animal level. At present, there is an increasing interest in multispectral bioluminescence tomography, since multispectral data acquisition could improve the BLT performance significantly. In view to the ill-posedness of BLT problem, we develop an optimal permissible source region strategy to constrain the possible solution of the source by utilizing spectrum character of bioluminescent source. Then a linear system to link the measured data with the unknown light source variables is established by utilizing the optimal permissible region strategy based on adaptive finite element analysis. Furthermore, singular value decomposition analysis is used for data dimensionality reduction and improving computational efficiency in multispectral case. The reconstructed speed and stability benefit from adaptive finite element, the permissible region strategy and singular value decomposition. In the numerical simulation, the heterogeneous phantom experiment has been used to evaluate the performance of the proposed algorithm with the Monte Carlo based synthetic data. The reconstruction results demonstrate the merits and potential of our methodology for localizing bioluminescent source.

An experimental cone-beam Micro-CT system for small animal imaging is presented in the paper. The system is designed to obtain high-resolution anatomic information and will be integrated with our bioluminescence tomography system. A flat panel X-ray detector (CMOS technology with a column CsI scintillator plate, 50 micron pixel size, 120 mm × 120 mm photodiode area) and a micro-focus X-ray source (13 to 40 μm of focal spot size) are used in the system. The object (mouse or rat) is placed on a three-degree (two translations and one rotation) programming stage and could be located to an accurate position in front of the detector. The large field of view (FOV) of the system allows us to acquire the whole body imaging of a normal mouse in one scanning which usually takes about 6 to 15 minutes. Raw data from X-ray detector show spatial variation caused by dark image offset, pixel gain and defective pixels, therefore data pre-processing is needed before reconstruction. Geometry calibrations are also used to reduce the artifacts caused by geometric misalignment. In order to accelerate FDK filtered backprojection method, we develop a reconstruction software using GPU hardware in our system. System spacial resolution and image uniformity and voxel noise have been assessed and mouse reconstruction images are illuminated in the paper. Experiment results show that this system is suitable for small animal imaging.

A prototype cone-beam micro-CT system for small animal imaging has been developed by our group recently, which consists of a microfocus X-ray source, a three-dimensional programmable stage with object holder, and a flat-panel X-ray detector. It has a large field of view (FOV), which can acquire the whole body imaging of a normal-size mouse in a single scan which usually takes about several minutes or tens of minutes. FDK method is adopted for 3D reconstruction with Graphics Processing Unit (GPU) acceleration. In order to reconstruct images with high spatial resolution and low artifacts, raw data preprocessing and geometry calibration are implemented before reconstruction. A method which utilizes a wire phantom to estimate the residual horizontal offset of the detector is proposed, and 1D point spread function is used to assess the performance of geometric calibration quantitatively. System spatial resolution, image uniformity and noise, and low contrast resolution have been studied. Mouse images with and without contrast agent are illuminated in this paper. Experimental results show that the system is suitable for small animal imaging and is adequate to provide high-resolution anatomic information for bioluminescence tomography to build a dual modality system.

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.

We present a sparse Bayesian reconstruction method based on multiple types of a priori information for multispectral bioluminescence tomography (BLT). In the Bayesian approach, five kinds of a priori information are incorporated, reducing the ill-posedness of BLT. Specifically, source sparsity characteristic is considered to promote reconstruction results. Considering the computational burden in the multispectral case, a series of strategies is adopted to improve computational efficiency, such as optimal permissible source region strategy and node model of the finite element method. The performance of the proposed algorithm is validated by a heterogeneous three-dimensional (3D) micron scale computed tomography atlas and a mouse-shaped phantom. Reconstructed results demonstrate the feasibility and effectiveness of the proposed algorithm.

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.

Tomographic bioluminescence imaging suffers from poor imaging robustness, since it is severely affected by selection of regularization parameter (RP) and initialization for source distribution. In this paper, a global-inexact-Newton based reconstruction methodology, which is regularized by a dynamic sparse term, is demonstrated for in vivo tomographic imaging. The novel method can enhance higher imaging reliability and efficiency. Mouse experimental reconstructions were performed to validate the proposed method. These results demonstrate that the source distribution can be accurately located over a wide range of values of RP and iteration initializations. Moreover, the reconstruction efficiency was also studied.