Illumination pattern optimization for fluorescence tomography: theory and simulation studies

Illumination pattern optimization in tomography based on the Kalman estimation filter and optimal experiment design

Tomography is widely used in medical imaging or industrial non-destructive testing applications. One costly and time consuming operation in any form of tomography is the process of data acquisition where a large number of measurements are made and collected data is used for image reconstruction. Data acquisition can slow down tomography to the point that the scanner cannot catch up with the speed of changes in the medium under test. By optimizing the information content of each measurement, we can reduce the number of measurements needed to achieve the target precision. Development of algorithms to optimize the information content of tomography measurements is the main goal of this article. Here, the dynamics of the medium and tomography measurements are formulated in the form of a Kalman estimation filter. A mathematical algorithm is developed to compute the optimal measurement matrix which minimizes the uncertainty left in the estimation of the distribution the tomography scanner is reconstructing. Results, as presented in the paper, show noticeable improvement is the quality of generated images when the medium is scanned by optimal measurements instead of traditional raster or random scanning protocols.

Simultaneous reconstruction of 3D fluorescence distribution and object surface using structured light illumination and dual-camera detection

Fluorescence molecular tomography (FMT) serves as a noninvasive modality for visualizing volumetric fluorescence distribution within biological tissues, thereby proving to be an invaluable imaging tool for preclinical animal studies. The conventional FMT relies upon a point-by-point raster scan strategy, enhancing the dataset for subsequent reconstruction but concurrently elongating the data acquisition process. The resultant diminished temporal resolution has persistently posed a bottleneck, constraining its utility in dynamic imaging studies. We introduce a novel system capable of simultaneous FMT and surface extraction, which is attributed to the implementation of a rapid line scanning approach and dual-camera detection. The system performance was characterized through phantom experiments, while the influence of scanning line density on reconstruction outcomes has been systematically investigated via both simulation and experiments. In a proof-of-concept study, our approach successfully captures a moving fluorescence bolus in three dimensions with an elevated frame rate of approximately 2.5 seconds per frame, employing an optimized scan interval of 5 mm. The notable enhancement in the spatio-temporal resolution of FMT holds the potential to broaden its applications in dynamic imaging tasks, such as surgical navigation.

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

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

Optimal data acquisition in tomography

In tomography, three-dimensional images of a medium are reconstructed from a set of two-dimensional projections. Each projection is the result of a measurement made by the scanner via radiating some form of energy and collecting the scattered field after interacting with the medium. The information content of these measurements is not equal, and one projection can be more informative than others. By choosing the most informative measurement at every step of scanning, an optimal tomography system can maximize the speed of data acquisition and temporal resolution of acquired images, reducing the operation cost and exposure to possible harmful radiations. The aim of this paper is to introduce mathematical algorithms that can be used to design measurements with optimal information content when imaging static or dynamically evolving objects.

Attention mechanism-based locally connected network for accurate and stable reconstruction in Cerenkov luminescence tomography

Cerenkov luminescence tomography (CLT) is a novel and highly sensitive imaging technique, which could obtain the three-dimensional distribution of radioactive probes to achieve accurate tumor detection. However, the simplified radiative transfer equation and ill-conditioned inverse problem cause a reconstruction error. In this study, a novel attention mechanism based locally connected (AMLC) network was proposed to reduce barycenter error and improve morphological restorability. The proposed AMLC network consisted of two main parts: a fully connected sub-network for providing a coarse reconstruction result, and a locally connected sub-network based on an attention matrix for refinement. Both numerical simulations and in vivo experiments were conducted to show the superiority of the AMLC network in accuracy and stability over existing methods (MFCNN, KNN-LC network). This method improved CLT reconstruction performance and promoted the application of machine learning in optical imaging research.

Optimal Scanning Protocol for Optical Tomography

Tomography is a two step process in which the sample under test is first scanned by the hardware of the system to acquire data and then the operating software reconstruct images from the gathered information. The main objective of this work is to optimize the scanning process to acquire maximum amount of information in each measurement when the system is scanning the sample. By exploiting our prior information about the sample and using estimation theory, we developed a systematic approach to implement the optimal scanning protocol. Results of this study provide strong evidence that the developed algorithms can speed up data acquisition. Also it is shown that the proposed method can reduce the impact of noise as well as improving the reconstruction error while performing less number of measurements.Clinical relevance- The proposed method can enhance data acquisition time, exposure dosage and cost of operation in medical applications of tomography.

Optimization of data acquisition operation in optical tomography based on estimation theory

The data acquisition process is occasionally the most time consuming and costly operation in tomography. Currently, raster scanning is still the common practice in making sequential measurements in most tomography scanners. Raster scanning is known to be slow and such scanners usually cannot catch up with the speed of changes when imaging dynamically evolving objects. In this research, we studied the possibility of using estimation theory and our prior knowledge about the sample under test to reduce the number of measurements required to achieve a given image quality. This systematic approach for optimization of the data acquisition process also provides a vision toward improving the geometry of the scanner and reducing the effect of noise, including the common state-dependent noise of detectors. The theory is developed in the article and simulations are provided to better display discussed concepts.

Musiré: multimodal simulation and reconstruction framework for the radiological imaging sciences

A software-based workflow is proposed for managing the execution of simulation and image reconstruction for SPECT, PET, CBCT, MRI, BLI and FMI packages in single and multimodal biomedical imaging applications. The workflow is composed of a Bash script, the purpose of which is to provide an interface to the user, and to organize data flow between dedicated programs for simulation and reconstruction. The currently incorporated simulation programs comprise GATE for Monte Carlo simulation of SPECT, PET and CBCT, SpinScenario for simulating MRI, and Lipros for Monte Carlo simulation of BLI and FMI. Currently incorporated image reconstruction programs include CASToR for SPECT and PET as well as RTK for CBCT. MetaImage (mhd) standard is used for voxelized phantom and image data format. Meshlab project (mlp) containers incorporating polygon meshes and point clouds defined by the Stanford triangle format (ply) are employed to represent anatomical structures for optical simulation, and to represent tumour cell inserts. A number of auxiliary programs have been developed for data transformation and adaptive parameter assignment. The software workflow uses fully automatic distribution to, and consolidation from, any number of Linux workstations and CPU cores. Example data are presented for clinical SPECT, PET and MRI systems using the Mida head phantom and for preclinical X-ray, PET and BLI systems employing the Digimouse phantom. The presented method unifies and simplifies multimodal simulation setup and image reconstruction management and might be of value for synergistic image research. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.

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.

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

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

Review of structured light in diffuse optical imaging

Diffuse optical imaging probes deep living tissue enabling structural, functional, metabolic, and molecular imaging. Recently, due to the availability of spatial light modulators, wide-field quantitative diffuse optical techniques have been implemented, which benefit greatly from structured light methodologies. Such implementations facilitate the quantification and characterization of depth-resolved optical and physiological properties of thick and deep tissue at fast acquisition speeds. We summarize the current state of work and applications in the three main techniques leveraging structured light: spatial frequency-domain imaging, optical tomography, and single-pixel imaging. The theory, measurement, and analysis of spatial frequency-domain imaging are described. Then, advanced theories, processing, and imaging systems are summarized. Preclinical and clinical applications on physiological measurements for guidance and diagnosis are summarized. General theory and method development of tomographic approaches as well as applications including fluorescence molecular tomography are introduced. Lastly, recent developments of single-pixel imaging methodologies and applications are reviewed.

Anatomical image-guided fluorescence molecular tomography reconstruction using kernel method

Fluorescence molecular tomography (FMT) is an important in vivo imaging modality to visualize physiological and pathological processes in small animals. However, FMT reconstruction is ill-posed and ill-conditioned due to strong optical scattering in deep tissues, which results in poor spatial resolution. It is well known that FMT image quality can be improved substantially by applying the structural guidance in the FMT reconstruction. An approach to introducing anatomical information into the FMT reconstruction is presented using the kernel method. In contrast to conventional methods that incorporate anatomical information with a Laplacian-type regularization matrix, the proposed method introduces the anatomical guidance into the projection model of FMT. The primary advantage of the proposed method is that it does not require segmentation of targets in the anatomical images. Numerical simulations and phantom experiments have been performed to demonstrate the proposed approach’s feasibility. Numerical simulation results indicate that the proposed kernel method can separate two FMT targets with an edge-to-edge distance of 1 mm and is robust to false-positive guidance and inhomogeneity in the anatomical image. For the phantom experiments with two FMT targets, the kernel method has reconstructed both targets successfully, which further validates the proposed kernel method.

Noninvasive Assessment of Elimination and Retention using CT-FMT and Kinetic Whole-body Modeling

Fluorescence-mediated tomography (FMT) is a quantitative three-dimensional imaging technique for preclinical research applications. The combination with micro-computed tomography (µCT) enables improved reconstruction and analysis. The aim of this study is to assess the potential of µCT-FMT and kinetic modeling to determine elimination and retention of typical model drugs and drug delivery systems.

We selected four fluorescent probes with different but well-known biodistribution and elimination routes: Indocyanine green (ICG), hydroxyapatite-binding OsteoSense (OS), biodegradable nanogels (NG) and microbubbles (MB). µCT-FMT scans were performed in twenty BALB/c nude mice (5 per group) at 0.25, 2, 4, 8, 24, 48 and 72 h after intravenous injection. Longitudinal organ curves were determined using interactive organ segmentation software and a pharmacokinetic whole-body model was implemented and applied to compute physiological parameters describing elimination and retention.

ICG demonstrated high initial hepatic uptake which decreased rapidly while intestinal accumulation appeared for around 8 hours which is in line with the known direct uptake by hepatocytes followed by hepatobiliary elimination. Complete clearance from the body was observed at 48 h. NG showed similar but slower hepatobiliary elimination because these nanoparticles require degradation before elimination can take place. OS was strongly located in the bones in addition to high signal in the bladder at 0.25 h indicating fast renal excretion. MB showed longest retention in liver and spleen and low signal in the kidneys likely caused by renal elimination or retention of fragments. Furthermore, probe retention was found in liver (MB, NG and OS), spleen (MB) and kidneys (MB and NG) at 72 h which was confirmed by ex vivo data. The kinetic model enabled robust extraction of physiological parameters from the organ curves.

In summary, µCT-FMT and kinetic modeling enable differentiation of hepatobiliary and renal elimination routes and allow for the noninvasive assessment of retention sites in relevant organs including liver, kidney, bone and spleen.

Development of computational small animal models and their applications in preclinical imaging and therapy research

The development of multimodality preclinical imaging techniques and the rapid growth of realistic computer simulation tools have promoted the construction and application of computational laboratory animal models in preclinical research. Since the early 1990s, over 120 realistic computational animal models have been reported in the literature and used as surrogates to characterize the anatomy of actual animals for the simulation of preclinical studies involving the use of bioluminescence tomography, fluorescence molecular tomography, positron emission tomography, single-photon emission computed tomography, microcomputed tomography, magnetic resonance imaging, and optical imaging. Other applications include electromagnetic field simulation, ionizing and nonionizing radiation dosimetry, and the development and evaluation of new methodologies for multimodality image coregistration, segmentation, and reconstruction of small animal images. This paper provides a comprehensive review of the history and fundamental technologies used for the development of computational small animal models with a particular focus on their application in preclinical imaging as well as nonionizing and ionizing radiation dosimetry calculations. An overview of the overall process involved in the design of these models, including the fundamental elements used for the construction of different types of computational models, the identification of original anatomical data, the simulation tools used for solving various computational problems, and the applications of computational animal models in preclinical research. The authors also analyze the characteristics of categories of computational models (stylized, voxel-based, and boundary representation) and discuss the technical challenges faced at the present time as well as research needs in the future.

Fast multislice fluorescence molecular tomography using sparsity-inducing regularization

Fluorescence molecular tomography (FMT) is a rapidly growing imaging method that facilitates the recovery of small fluorescent targets within biological tissue. The major challenge facing the FMT reconstruction method is the ill-posed nature of the inverse problem. In order to overcome this problem, the acquisition of large FMT datasets and the utilization of a fast FMT reconstruction algorithm with sparsity regularization have been suggested recently. Therefore, the use of a joint L1/total-variation (TV) regularization as a means of solving the ill-posed FMT inverse problem is proposed. A comparative quantified analysis of regularization methods based on L1-norm and TV are performed using simulated datasets, and the results show that the fast composite splitting algorithm regularization method can ensure the accuracy and robustness of the FMT reconstruction. The feasibility of the proposed method is evaluated in an in vivo scenario for the subcutaneous implantation of a fluorescent-dye-filled capillary tube in a mouse, and also using hybrid FMT and x-ray computed tomography data. The results show that the proposed regularization overcomes the difficulties created by the ill-posed inverse problem.

Generalized mesh-based Monte Carlo for wide-field illumination and detection via mesh retessellation

Monte Carlo methods are commonly used as the gold standard in modeling photon transport through turbid media. With the rapid development of structured light applications, an accurate and efficient method capable of simulating arbitrary illumination patterns and complex detection schemes over large surface area is in great need. Here we report a generalized mesh-based Monte Carlo algorithm to support a variety of wide-field illumination methods, including spatial-frequency-domain imaging (SFDI) patterns and arbitrary 2-D patterns. The extended algorithm can also model wide-field detectors such as a free-space CCD camera. The significantly enhanced flexibility of source and detector modeling is achieved via a fast mesh retessellation process that combines the target domain and the source/detector space in a single tetrahedral mesh. Both simulations of complex domains and comparisons with phantom measurements are included to demonstrate the flexibility, efficiency and accuracy of the extended algorithm. Our updated open-source software is provided at http://mcx.space/mmc.

Wide-field fluorescence molecular tomography with compressive sensing based preconditioning

Wide-field optical tomography based on structured light illumination and detection strategies enables efficient tomographic imaging of large tissues at very fast acquisition speeds. However, the optical inverse problem based on such instrumental approach is still ill-conditioned. Herein, we investigate the benefit of employing compressive sensing-based preconditioning to wide-field structured illumination and detection approaches. We assess the performances of Fluorescence Molecular Tomography (FMT) when using such preconditioning methods both in silico and with experimental data. Additionally, we demonstrate that such methodology could be used to select the subset of patterns that provides optimal reconstruction performances. Lastly, we compare preconditioning data collected using a normal base that offers good experimental SNR against that directly acquired with optimal designed base. An experimental phantom study is provided to validate the proposed technique.

Experimental Comparison of Continuous-Wave and Frequency-Domain Fluorescence Tomography in a Commercial Multi-Modal Scanner

The performance evaluation of a variety of small animal tomography measurement approaches and algorithms for recovery of fluorescent absorption cross section has not been conducted. Herein, we employed an intensified CCD system installed in a commercial small animal CT (Computed Tomography) scanner to compare image reconstructions from time-independent, continuous wave (CW) measurements and from time-dependent, frequency domain (FD) measurements in a series of physical phantoms specifically designed for evaluation. Comparisons were performed as a function of (1) number of projections, (2) the level of preprocessing filters used to improve the signal-to-noise ratio (SNR), (3) endogenous heterogeneity of optical properties, as well as in the cases of (4) two fluorescent targets and (5) a mouse-shaped phantom. Assessment of quantitative recovery of fluorescence absorption cross section was performed using a fully parallel, regularization-free, linear reconstruction algorithm with diffusion approximation (DA) and high order simplified spherical harmonics ( SPN) approximation to the radiative transport equation (RTE). The results show that while FD measurements may result in superior image reconstructions over CW measurements, data acquisition times are significantly longer, necessitating further development of multiple detector/source configurations, improved data read-out rates, and detector technology. FD measurements with SP3 reconstructions enabled better quantitative recovery of fluorescent target strength, but required increased computational expense. Despite the developed parallel reconstruction framework being able to achieve more than 60 times speed increase over sequential implementation, further development in faster parallel acceleration strategies for near-real time and real-time image recovery and more precise forward solution is necessary.

Fluorescence molecular tomography in the second near-infrared window

Fluorescence molecular tomography (FMT), an in vivo noninvasive imaging technology, can provide localization and quantification information for deep fluorophores. Light at wavelengths in the near-infrared (NIR-I) window from 650 nm to 950 nm has conventionally been chosen for FMT. In this study, we introduced longer NIR wavelengths within the 1100 nm to 1400 nm range, known as the "second NIR spectral window" (NIR-II). A singular-value analysis method was used to demonstrate the utility and advantages of using the NIR-II for FMT, and experiments showed an improvement in the spatial resolution in phantom studies.

Hadamard multiplexed fluorescence tomography

Depth-resolved three-dimensional (3D) reconstruction of fluorophore-tagged inclusions in fluorescence tomography (FT) poses a highly ill-conditioned problem as depth information must be extracted from boundary data. Due to the ill-posed nature of the FT inverse problem, noise and errors in the data can severely impair the accuracy of the 3D reconstructions. The signal-to-noise ratio (SNR) of the FT data strongly affects the quality of the reconstructions. Additionally, in FT scenarios where the fluorescent signal is weak, data acquisition requires lengthy integration times that result in excessive FT scan periods. Enhancing the SNR of FT data contributes to the robustness of the 3D reconstructions as well as the speed of FT scans. A major deciding factor in the SNR of the FT data is the power of the radiation illuminating the subject to excite the administered fluorescent reagents. In existing single-point illumination FT systems, the source power level is limited by the skin maximum radiation exposure levels. In this paper, we introduce and study the performance of a multiplexed fluorescence tomography system with orders-of-magnitude enhanced data SNR over existing systems. The proposed system allows for multi-point illumination of the subject without jeopardizing the information content of the FT measurements and results in highly robust reconstructions of fluorescent inclusions from noisy FT data. Improvements offered by the proposed system are validated by numerical and experimental studies.

Use of optical imaging to progress novel therapeutics to the clinic

There is an undisputed need for employment and improvement of robust technology for real-time analyses of therapeutic delivery and responses in clinical translation of gene and cell therapies. Over the past decade, optical imaging has become the in vivo imaging modality of choice for many preclinical laboratories due to its efficiency, practicality and affordability, while more recently, the clinical potential for this technology is becoming apparent. This review provides an update on the current state of the art in in vivo optical imaging and discusses this rapidly improving technology in the context of it representing a translation enabler or indeed a future clinical imaging modality in its own right.

Spatial-frequency-compression scheme for diffuse optical tomography with dense sampling dataset

Dense sampling of illumination and detection offers an effective way of improving the image-reconstruction performance of near-infrared diffuse optical tomography (DOT) at a cost of lengthy computation times. In this paper, we describe a fast DOT scheme for reconstructing the absorption coefficient image of a slab medium from dense sampling of both illumination and detection in the noncontact DOT. The proposed method is carried out with spatial-frequency encoding in both the source and detection spaces, and involves a spatial-frequency-compression (SFC) strategy for selecting the useful spatial frequency based on the tissue transfer function. The method is expected to considerably reduce the calculation time for reconstruction while improving the quality of the reconstructed images. Results from the simulated data show that the speed for absorption reconstruction with the proposed SFC method is more than 400 times faster than that with the conventional one. A noncontact DOT system for dense sampling of both illumination and detection is developed by using laser raster scanning and CCD-based data acquisition. Experimental measurements on several solid phantoms demonstrate that a high quantitativeness ratio can be obtained from the proposed method thanks to reduction of the ill-posedness of the inverse calculation. It takes less than 20 s for the proposed method to experimentally reconstruct one absorption image from a 256×256-sized dataset, which would take a few hours with the conventional method.

Adaptive wide-field optical tomography

We describe a wide-field optical tomography technique, which allows the measurement-guided optimization of illumination patterns for enhanced reconstruction performances. The iterative optimization of the excitation pattern aims at reducing the dynamic range in photons transmitted through biological tissue. It increases the number of measurements collected with high photon counts resulting in a dataset with improved tomographic information. Herein, this imaging technique is applied to time-resolved fluorescence molecular tomography for preclinical studies. First, the merit of this approach is tested by in silico studies in a synthetic small animal model for typical illumination patterns. Second, the applicability of this approach in tomographic imaging is validated in vitro using a small animal phantom with two fluorescent capillaries occluded by a highly absorbing inclusion. The simulation study demonstrates an improvement of signal transmitted (∼2 orders of magnitude) through the central portion of the small animal model for all patterns considered. A corresponding improvement in the signal at the emission wavelength by 1.6 orders of magnitude demonstrates the applicability of this technique for fluorescence molecular tomography. The successful discrimination and localization (∼1  mm error) of the two objects with higher resolution using the optimized patterns compared with nonoptimized illumination establishes the improvement in reconstruction performance when using this technique.

Greedy reconstruction algorithm for fluorescence molecular tomography by means of truncated singular value decomposition conversion

Fluorescence molecular tomography (FMT) is a promising imaging modality that enables three-dimensional visualization of fluorescent targets in vivo in small animals. L2-norm regularization methods are usually used for severely ill-posed FMT problems. However, the smoothing effects caused by these methods result in continuous distribution that lacks high-frequency edge-type features and hence limits the resolution of FMT. In this paper, the sparsity in FMT reconstruction results is exploited via compressed sensing (CS). First, in order to ensure the feasibility of CS for the FMT inverse problem, truncated singular value decomposition (TSVD) conversion is implemented for the measurement matrix of the FMT problem. Then, as one kind of greedy algorithm, an ameliorated stagewise orthogonal matching pursuit with gradually shrunk thresholds and a specific halting condition is developed for the FMT inverse problem. To evaluate the proposed algorithm, we compared it with a TSVD method based on L2-norm regularization in numerical simulation and phantom experiments. The results show that the proposed algorithm can obtain higher spatial resolution and higher signal-to-noise ratio compared with the TSVD method.

Mesh-based Monte Carlo method in time-domain widefield fluorescence molecular tomography

We evaluated the potential of mesh-based Monte Carlo (MC) method for widefield time-gated fluorescence molecular tomography, aiming to improve accuracy in both shape discretization and photon transport modeling in preclinical settings. An optimized software platform was developed utilizing multithreading and distributed parallel computing to achieve efficient calculation. We validated the proposed algorithm and software by both simulations and in vivo studies. The results establish that the optimized mesh-based Monte Carlo (mMC) method is a computationally efficient solution for optical tomography studies in terms of both calculation time and memory utilization. The open source code, as part of a new release of mMC, is publicly available at http://mcx.sourceforge.net/mmc/.

Joint L1 and total variation regularization for fluorescence molecular tomography

Fluorescence molecular tomography (FMT) is an imaging modality that exploits the specificity of fluorescent biomarkers to enable 3D visualization of molecular targets and pathways in vivo in small animals. Owing to the high degree of absorption and scattering of light through tissue, the FMT inverse problem is inherently ill-conditioned making image reconstruction highly susceptible to the effects of noise and numerical errors. Appropriate priors or penalties are needed to facilitate reconstruction and to restrict the search space to a specific solution set. Typically, fluorescent probes are locally concentrated within specific areas of interest (e.g., inside tumors). The commonly used L(2) norm penalty generates the minimum energy solution, which tends to be spread out in space. Instead, we present here an approach involving a combination of the L(1) and total variation norm penalties, the former to suppress spurious background signals and enforce sparsity and the latter to preserve local smoothness and piecewise constancy in the reconstructed images. We have developed a surrogate-based optimization method for minimizing the joint penalties. The method was validated using both simulated and experimental data obtained from a mouse-shaped phantom mimicking tissue optical properties and containing two embedded fluorescent sources. Fluorescence data were collected using a 3D FMT setup that uses an EMCCD camera for image acquisition and a conical mirror for full-surface viewing. A range of performance metrics was utilized to evaluate our simulation results and to compare our method with the L(1), L(2) and total variation norm penalty-based approaches. The experimental results were assessed using the Dice similarity coefficients computed after co-registration with a CT image of the phantom.

Compressive diffuse optical tomography: noniterative exact reconstruction using joint sparsity

Diffuse optical tomography (DOT) is a sensitive and relatively low cost imaging modality that reconstructs optical properties of a highly scattering medium. However, due to the diffusive nature of light propagation, the problem is severely ill-conditioned and highly nonlinear. Even though nonlinear iterative methods have been commonly used, they are computationally expensive especially for three dimensional imaging geometry. Recently, compressed sensing theory has provided a systematic understanding of high resolution reconstruction of sparse objects in many imaging problems; hence, the goal of this paper is to extend the theory to the diffuse optical tomography problem. The main contributions of this paper are to formulate the imaging problem as a joint sparse recovery problem in a compressive sensing framework and to propose a novel noniterative and exact inversion algorithm that achieves the l(0) optimality as the rank of measurement increases to the unknown sparsity level. The algorithm is based on the recently discovered generalized MUSIC criterion, which exploits the advantages of both compressive sensing and array signal processing. A theoretical criterion for optimizing the imaging geometry is provided, and simulation results confirm that the new algorithm outperforms the existing algorithms and reliably reconstructs the optical inhomogeneities when we assume that the optical background is known to a reasonable accuracy.

Multiple-view fluorescence optical tomography reconstruction using compression of experimental data

We report on the experimental demonstration of a fast reconstruction method for multiview fluorescence diffuse optical tomography by using a wavelet-based data compression. We experimentally demonstrate that the use of data compression combined with the multiview approach makes it possible to perform a fast reconstruction of high quality. A structured illumination approach, guided by the compression scheme, has been adopted to further reduce the acquisition time. The reconstruction algorithm is based on the finite element method, and hence is suitable for samples of any arbitrary shape.

Excitation-resolved fluorescence tomography with simplified spherical harmonics equations

Fluorescence tomography (FT) reconstructs the three-dimensional (3D) fluorescent reporter probe distribution inside biological tissue. These probes target molecules of biological function, e.g. cell surface receptors or enzymes, and emit fluorescence light upon illumination with an external light source. The fluorescence light is detected on the tissue surface and a source reconstruction algorithm based on the simplified spherical harmonics (SP(N)) equations calculates the unknown 3D probe distribution inside tissue. While current FT approaches require multiple external sources at a defined wavelength range, the proposed FT method uses only a white light source with tunable wavelength selection for fluorescence stimulation and further exploits the spectral dependence of tissue absorption for the purpose of 3D tomographic reconstruction. We will show the feasibility of the proposed hyperspectral excitation-resolved fluorescence tomography method with experimental data. In addition, we will demonstrate the performance and limitations of such a method under ideal and controlled conditions by means of a digital mouse model and synthetic measurement data. Moreover, we will address issues regarding the required amount of wavelength intervals for fluorescent source reconstruction. We will explore the impact of assumed spatially uniform and nonuniform optical parameter maps on the accuracy of the fluorescence source reconstruction. Last, we propose a spectral re-scaling method for overcoming the observed limitations in reconstructing accurate source distributions in optically non-uniform tissue when assuming only uniform optical property maps for the source reconstruction process.

Full-field time-resolved fluorescence tomography of small animals

In this experimental investigation, we explore the feasibility of using wide-field illumination for time-resolved fluorescence molecular tomography. The performance of wide-field patterns with a time-resolved imaging platform is investigated in vitro and in a small animal model. A Monte Carlo-based forward model is employed to reconstruct fluorescence yield based on time-gated datasets. An improvement in resolution and quantification when using the time-gate data type compared to the commonly used cw data type is demonstrated in vitro. Furthermore, the feasibility of wide-field strategies for fluorescence preclinical applications is established by an accurate localization of a fluorescent inclusion implanted in the chest cavity of a murine model.

Fast 3D optical reconstruction in turbid media using spatially modulated light

A method to perform fast 3-D optical reconstruction, based on structured light, in thick samples is demonstrated and experimentally validated. The experimental and reconstruction procedure, based on Finite Elements Method, used to reconstruct absorbing heterogeneities, with arbitrary arrangement in space, is discussed. In particular we demonstrated that a 2D sampling of the source Fourier plane is required to improve the imaging capability.

Development of an optical imaging platform for functional imaging of small animals using wide-field excitation

The design and characterization of a time-resolved functional imager using a wide-field excitation scheme for small animal imaging is described. The optimal operation parameters are established based on phantom studies. The performance of the platform for functional imaging and the simultaneous 3D reconstruction of absorption and scattering coefficients is investigated in vitro.