KEYWORDS: Point spread functions, Particles, Nanoparticles, Phase shift keying, 3D tracking, Objectives, Signal to noise ratio, Optical engineering, Diffusion, Superposition
A well-established method for 3D nanoparticle tracking is the double-helix point spread function (DH-PSF) engineering, which uses additional optical elements to make the PSF exhibit different rotation angles with varying depths of nanoparticles. Splicing the symmetric spiral phases together using the phase splicing method can generate the modulated phase mask of 2π-DH-PSF. The 2π-DH-PSF has a linear rotation rate at each axial position and has a larger rotation angle, showing a more accurate rotation angle and depth translation. Experiments with a phase-only spatial light modulator demonstrate the potential of the 2π-DH-PSF. Finally, we successfully conducted 3D nanoparticle tracking experiments at 8-μm depth with a numerical aperture of 1.4, showing its great potential.
Photoacoustic computed tomography (PACT) is a rapidly developing biomedical imaging modality and has attracted substantial attention in recent years. Image reconstruction from photoacoustic projections plays a critical role in image formation in PACT. Here we review six major classes of image reconstruction approaches developed in the past three decades, including delay and sum, filtered back projection, series expansion, time reversal, iterative reconstruction, and deep-learning-based reconstruction. The principal ideas and implementations of the algorithms are summarized, and their reconstruction performances under different imaging scenarios are compared. Major challenges, future directions, and perspectives for the development of image reconstruction algorithms in PACT are also discussed. This review provides a self-contained reference guide for beginners and specialists in the photoacoustic community, to facilitate the development and application of novel photoacoustic image reconstruction algorithms.
As a key and unique feature, anisotropy is the variability of material properties in different directions caused by molecular conformation and structural arrangement. The optical properties of anisotropic materials include birefringence and dichroism, and the characterization of these properties can be used to detect changes in the microstructure of materials. Conventional polarized optics imaging and photoacoustic imaging are based on scattering and contact measurements respectively, limiting many applications. Photoacoustic remote sensing (PARS) is an all-optical, non-contact photoacoustic measurement technique that provides specific light absorption contrast without coupling or labeling. However, PARS usually ignores tissue uptake anisotropy, which may result in missing some unique tissue properties and information. To obtain more dimensional information on targets, here we propose polarized photoacoustic remote sensing (P-PARS) microscopy. The system uses linearly polarized light in different directions as the excitation source and indicates the anisotropic optical absorption of the target by detecting changes in the reflected intensity of the interrogation light. It can simultaneously provide images of optical absorption contrast and the degree of anisotropy. Experimental results of testing materials and biological tissues demonstrated the feasibility and stability of the P-PARS. This method provides a new noncontact label-free strategy for anisotropy detection, prefiguring important potential for anisotropic material inspection and biological tissue imaging.
Hypoxia in malignant tumors can have an inhibitory effect on photodynamic therapy, chemotherapy, radiotherapy, immunotherapy, and chemodynamic therapy. In previous studies, nanoparticle platforms combined with drugs or direct oxygen-carrying to increase oxygen content are usually used. Here, we designed to improve tumor hypoxia by injecting human-derived hemoglobin-based oxygen carriers (h-HBOC) synthesized from expired human blood or human placental blood, and real-time quantitative oxygen concentration monitoring was achieved with the help of photoacoustic mesofunctional imaging, and the oxygen concentration was calculated using a multispectral unmixing method. Firstly, we combined photoacoustic mesoscopy to monitor the blood oxygen concentration of the same batch of mice injected with different concentrations of h-HBOC, and verified the positive correlation between the h-HBOC concentration and the blood oxygen level without affecting the hemoglobin of the animals themselves. The relative optimal dose of h- HBOC for targeting 4T1 tumor line mice was determined to be 600 mg/kg, with the sixth hour after h-HBOC injection being the relative optimal moment of treatment. A method for determining the optimal moment of tumor microenvironment by real-time monitoring of blood oxygenation in malignant tumors has been developed to determine the optimal time of treatment for therapeutic means. This therapeutic strategy can formulate differentiated and visualized therapeutic strategies for individual individuals according to different tumor lineages and bio-individuals, bridging the individual differences and obtaining the optimal therapeutic plan. Since the improvement of tumor microenvironment by h-HBOC is sustained in the long term and can be used to improve photodynamic therapy, and chemotherapy and radiotherapy have positive effects, this strategy has great potential and application for improving multimodal treatment of malignant tumors in the future.
Radiation at terahertz frequencies can be used to analyze the structural dynamics of water and biomolecules, but applying the technique to aqueous solutions and tissues remains challenging since terahertz radiation is strongly absorbed by water. While this absorption enables certain analyses, such as the structure of water and its interactions with biological solutes, it limits the thickness of samples that can be analyzed, and it drowns out weaker signals from biomolecules of interest. We present a method for analyzing water-rich samples via time-domain terahertz optoacoustics over a 104-fold thickness ranging from microns to centimeters. We demonstrate that adjusting the temperature to alter the terahertz optoacoustic (THz-OA) signal of water improves the sensitivity with which it can be analyzed and, conversely, can reduce or even “silence” its signal. Temperature-manipulated THz-OA signals of aqueous solutions allow detection of solutes such as ions with an order of magnitude greater sensitivity than terahertz time-domain spectroscopy, and potentially provide more characteristic parameters related to both terahertz absorption and ultrasonic propagation. Terahertz optoacoustics may be a powerful tool for spectroscopy and potential imaging of aqueous solutions and tissues to explore molecular interactions and biochemical processes.
Diffuse Optical Tomography (DOT) is a promising non-invasive optical imaging technology that can provide structural and functional information of biological tissues. Since the diffused light undergoes multiple scattering in biological tissues, and the boundary measurements are limited, the reverse problem of DOT is ill-posed and ill-conditioned. In order to overcome these limitations, two types of neural networks, back-propagation neural network (BPNN) and stacked autoencoder (SAE) were applied in DOT image reconstruction, which use the internal optical properties distribution and the boundary measurement of biological tissues as the input and output data sets respectively to adjust the neural network parameters, and directly establish a nonlinear mapping of the input and output. To verify the effectiveness of the methods, a series of numerical simulation experiments were conducted, and the experimental results were quantitatively assessed, which demonstrated that both methods can accurately predict the position and size of the inclusion, especially in the case of higher absorption contrast. As a whole, SAE can get better reconstructed image results than BPNN and the training time was only a quarter of BPNN.
Fluorescence pharmacokinetics can analyze the absorption, distribution, metabolism and other pharmacokinetic processes of fluorescence agents in biological tissues over time, which can provide more specific and quantitative physiological and pathological information for the evaluation of organ function. This paper is devoted to studying pharmacokinetics of indocyanine green (ICG) in healthy mice and mice with acute alcoholic liver injury based on a home-made dynamic diffuse fluorescence tomography system that possesses high sensitivity and large dynamic measurement range on account of digital lock-in-photon-counting technique. In this study, four-week-old Kunming mice were randomly divided into experimental and control groups. The time-varying distribution of ICG in mice was obtained by diffuse fluorescence tomography reconstruction, and the pharmacokinetic parameters were further extracted from the ICG concentration-time curve. The results showed that the dynamic diffuse fluorescence tomography system successfully captured the ICG metabolism process in mouse liver, and the ICG excretion rate demonstrated an obvious difference between healthy mice and the mice with acute alcoholic liver injury.
Quantitative photoacoustic imaging (QPAI) is a hybrid imaging technique aimed at reconstructing optical parameters from photoacoustic signals detected around the biological tissues. The recovery of optical parameters is a nonlinear, ill-posed inverse problem which is usually solved by iterative optimization methods based on the error minimization strategy. Most of the iterative algorithms are empirical and computationally expensive, leading to inadequate performance in practical application. In this work, we propose a deep learning-based QPAI approach to efficiently recover the optical absorption coefficient of biological tissues from the reconstructed result of initial pressure. The method involves a U-Net architecture based on the fully convolutional neural network. The Monte Carlo simulation with the wide-field illumination has been used to generate simulation data for the network training. The feasibility of the proposed method was demonstrated through numerical simulations, and its applicability to quantitatively reconstruct the distribution of optical absorption in the practical situation is further verified in phantom experiments. High image performance of this method in accuracy, efficiency and fidelity from both simulated and experimental results, suggests the enormous potential in biomedical applications in the future.
Pharmacokinetic diffuse fluorescence tomography (DFT) can provide helpful diagnostic information for tumor differentiation and monitoring. Among the methods of achieving pharmacokinetic parameters, adaptive extended Kalman filtering (AEKF) as a nonlinear filter method demonstrates the merits of quantitativeness, noise-robustness, and initialization independence. In this paper, indirect and direct AEKF schemes based on a commonly used two-compartment model were studied to extract pharmacokinetic parameters from simulation data. To assess the effect of metabolic rate on the reconstruction results, a series of numerical simulation experiments with the metabolic time range from 4.16 min to 38 min were carried out and the results obtained by the two schemes were compared. The results demonstrate that when the metabolic time is longer than 18 min, the pharmacokinetic-rate estimates of two schemes are similar; however, when the metabolic time is shorter than 5 min, the pharmacokinetic parameters obtained by the indirect scheme are far from the true value and even unavailable.
Diffuse optical tomography (DOT) is a novel functional imaging technique that has the vital clinical application. Aiming at the problems in DOT technology, we developed a three-wavelength continuous wave DOT system with high sensitivity and temporal resolution by adopting photo-multiple tube and photon counting detection, as well as lock-in technique. To assess the performance of the system, we conducted a series of cylindrical phantom experiments with optical properties that closely match those of human tissue, and obtained the reconstruction images by combining with our developed imaging scheme. The experimental results show that the position and size of the reconstructed targets are accurate, demonstrating the feasibility of the system. Additionally, the sensitivity, quantitativeness and spatial resolution of the imaging system were assessed by varying the target-to-background contrasting absorption contrast and target size. These preliminary results indicate that the system is scientifically capable of subcentimeter resolution imaging of low-contrast the lesion from the normal background.
The characteristics of the transducer, such as the transducer shape, have a significant impact on the image performance in optoacoustic (photoacoustic) imaging. Several reconstruction algorithms have considered the shape of the transducer in the optoacoustic reconstruction process, showing the improvement in image quality compared to reconstruction procedures with the point detector approximation. One flexible approach assumes the surface of transducer that consists of a set of surface elements. However, this approach suffers from long computation time and excessive memory consumption, especially for model-based reconstruction strategies. Herein, we present a modified model-based reconstruction algorithm using a virtual parallel-projection method, for the optoacoustic imaging system with flat detector. In this case, the sum of the surface elements' model matrixes can be replaced by a virtual parallel-projection model matrix, in order to reduce the reconstruction time and memory consumption. The proposed method has been performed on numerical simulations, phantom experiments of microspheres with the diameter of 200 μm and in vivo experiments in mice. The reconstruction results of proposed method show the similar image quality as the results of the traditional reconstruction method setting surface elements, while the computation time and memory requirements have been efficiently decreased.
To fully realize the potential of photoacoustic tomography (PAT) in preclinical and clinical applications, rapid measurements and robust reconstructions are needed. Sparse-view measurements have been adopted effectively to accelerate the data acquisition. However, since the reconstruction from the sparse-view sampling data is challenging, both of the effective measurement and the appropriate reconstruction should be taken into account. In this study, we present an iterative sparse-view PAT reconstruction scheme where a virtual parallel-projection concept matching for the proposed measurement condition is introduced to help to achieve the “compressive sensing” procedure of the reconstruction, and meanwhile the spatially adaptive filtering fully considering the a priori information of the mutually similar blocks existing in natural images is introduced to effectively recover the partial unknown coefficients in the transformed domain. Therefore, the sparse-view PAT images can be reconstructed with higher quality compared with the results obtained by the universal back-projection (UBP) algorithm in the same sparse-view cases. The proposed approach has been validated by simulation experiments, which exhibits desirable performances in image fidelity even from a small number of measuring positions.
Quantitative photoacoustic tomography (q-PAT) is a nontrivial technique can be used to reconstruct the absorption image with a high spatial resolution. Several attempts have been investigated by setting point sources or fixed-angle illuminations. However, in practical applications, these schemes normally suffer from low signal-to-noise ratio (SNR) or poor quantification especially for large-size domains, due to the limitation of the ANSI-safety incidence and incompleteness in the data acquisition. We herein present a q-PAT implementation that uses multi-angle light-sheet illuminations and a calibrated iterative multi-angle reconstruction. The approach can acquire more complete information on the intrinsic absorption and SNR-boosted photoacoustic signals at selected planes from the multi-angle wide-field excitations of light-sheet. Therefore, the sliced absorption maps over whole body can be recovered in a measurementflexible, noise-robust and computation-economic way. The proposed approach is validated by the phantom experiment, exhibiting promising performances in image fidelity and quantitative accuracy.
Photoacoustic mesoscopy (PAMe), offering high-resolution (sub-100-μm) and high optical contrast imaging at the depth of 1-10 mm, generally obtains massive collection data using a high-frequency focused ultrasonic transducer. The spatial impulse response (SIR) of this focused transducer causes the distortion of measured signals in both duration and amplitude. Thus, the reconstruction method considering the SIR needs to be investigated in the computation-economic way for PAMe. Here, we present a modified back-projection algorithm, by introducing a SIR-dependent calibration process using a non-satationary convolution method. The proposed method is performed on numerical simulations and phantom experiments of microspheres with diameter of both 50 μm and 100 μm, and the improvement of image fidelity of this method is proved to be evident by methodology parameters. The results demonstrate that, the images reconstructed when the SIR of transducer is accounted for have higher contrast-to-noise ratio and more reasonable spatial resolution, compared to the common back-projection algorithm.
The purpose of this work is to introduce and study a novel x-ray beam irradiation pattern for X-ray Luminescence Computed Tomography (XLCT), termed multiple intensity-weighted narrow-beam irradiation. The proposed XLCT imaging method is studied through simulations of x-ray and diffuse lights propagation. The emitted optical photons from X-ray excitable nanophosphors were collected by optical fiber bundles from the right-side surface of the phantom. The implementation of image reconstruction is based on the simulated measurements from 6 or 12 angular projections in terms of 3 or 5 x-ray beams scanning mode. The proposed XLCT imaging method is compared against the constant intensity weighted narrow-beam XLCT. From the reconstructed XLCT images, we found that the Dice similarity and quantitative ratio of targets have a certain degree of improvement. The results demonstrated that the proposed method can offer simultaneously high image quality and fast image acquisition.
Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging method to monitor the cerebral hemodynamic through the optical changes measured at the scalp surface. It has played a more and more important role in psychology and medical imaging communities. Real-time imaging of brain function using NIRS makes it possible to explore some sophisticated human brain functions unexplored before. Kalman estimator has been frequently used in combination with modified Beer-Lamber Law (MBLL) based optical topology (OT), for real-time brain function imaging. However, the spatial resolution of the OT is low, hampering the application of OT in exploring some complicated brain functions. In this paper, we develop a real-time imaging method combining diffuse optical tomography (DOT) and Kalman estimator, much improving the spatial resolution. Instead of only presenting one spatially distributed image indicating the changes of the absorption coefficients at each time point during the recording process, one real-time updated image using the Kalman estimator is provided. Its each voxel represents the amplitude of the hemodynamic response function (HRF) associated with this voxel. We evaluate this method using some simulation experiments, demonstrating that this method can obtain more reliable spatial resolution images. Furthermore, a statistical analysis is also conducted to help to decide whether a voxel in the field of view is activated or not.
Multispectral optoacoustic mesoscopy (MSOM) has been recently introduced for cancer imaging, it has the potential for high resolution imaging of cancer development in vivo, at depths beyond the diffusion limit. Based on spectral features, optoacoustic imaging is capable of visualizing angiogenesis and imaging cancer heterogeneity of malignant tumors through endogenous hemoglobin. However, high-resolution structural and functional imaging of whole tumor mass is limited by modest penetration and image quality, due to the insufficient capability of ultrasound detectors and the twodimensional scan geometry. In this study, we introduce a novel multi-spectral optoacoustic mesoscopy (MSOM) for imaging subcutaneous or orthotopic tumors implanted in lab mice, with the high-frequency ultrasound linear array and a conical scanning geometry. Detailed volumetric images of vasculature and oxygen saturation of tissue in the entire tumors are obtained in vivo, at depths up to 10 mm with the desirable spatial resolutions approaching 70μm. This unprecedented performance enables the visualization of vasculature morphology and hypoxia conditions has been verified with ex vivo studies. These findings demonstrate the potential of MSOM for preclinical oncological studies in deep solid tumors to facilitate the characterization of tumor’s angiogenesis and the evaluation of treatment strategies.
X-ray phase contrast imaging (XPCI) is a novel method that exploits the phase shift for the incident X-ray to form an image. Various XPCI methods have been proposed, among which, in-line phase contrast imaging (IL-PCI) is regarded as one of the most promising clinical methods. The contrast of the interface is enhanced due to the introduction of the boundary fringes in XPCI, thus it is generally used to evaluate the image quality of XPCI. But the contrast is a comprehensive index and it does not reflect the information of image quality in the frequency range. The modulation transfer function (MTF), which is the Fourier transform of the system point spread function, is recognized as the metric to characterize the spatial response of conventional X-ray imaging system. In this work, MTF is introduced into the image quality evaluation of the IL-PCI system. Numerous simulations based on Fresnel - Kirchhoff diffraction theory are performed with varying system settings and the corresponding MTFs were calculated for comparison. The results show that MTF can provide more comprehensive information of image quality comparing to contrast in IL-PCI.
Phase contrast x-ray imaging techniques have shown the ability to overcome the weakness of the low sensitivity of conventional x-ray imaging. Among them, in-line phase contrast imaging, blessed with simplicity of arrangement, is deemed to be a promising technique in clinical application. To obtain quantitative information from in-line phase contrast images, numerous phase-retrieval techniques have been developed. The theories of these phase-retrieval techniques are mostly proposed on the basis of the ideal detector and the noise-free environment. However, in practice, both detector resolution and system noise would have impacts on the performance of these phase-retrieval methods. To assess the impacts of above-mentioned factors, we include the effects of Gaussian shaped detectors varying in the full width at half maximum (FWHM) and system noise at different levels into numerical simulations. The performance of the phase-retrieval methods under such conditions is evaluated by the root mean square error. The results demonstrate that an increase in the detector FWHM or noise level degrades the effect of phase retrieval, especially for objects in small size.
There is a direct evidence that the radiation doses associated with CT scans are associated with an increase in cancer risk. To reduce the radiation dose and simultaneously maintain the CT reconstruction quality, numerous algorithms have been proposed such as compressive sensing (CS) technique. CS theory asserts that one can recover certain signals and images from far fewer samples or measurements than traditional methods use. In this study, we mainly consider the relationship between the CT reconstruction quality and two undersampled scan types of CS technique, i.e., the sparse-view scan and limited-view scan. The results demonstrate that an appropriate selection of scan type of CS technique can effectively control the radiation dose.
In vivo tomographic imaging of the fluorescence pharmacokinetic parameters in tissues can provide additional specific and quantitative physiological and pathological information to that of fluorescence concentration. This modality normally requires a highly-sensitive diffuse fluorescence tomography (DFT) working in dynamic way to finally extract the pharmacokinetic parameters from the measured pharmacokinetics-associated temporally-varying boundary intensity. This paper is devoted to preliminary experimental validation of our proposed direct reconstruction scheme of instantaneous sampling based pharmacokinetic-DFT: A highly-sensitive DFT system of CT-scanning mode working with parallel four photomultiplier-tube photon-counting channels is developed to generate an instantaneous sampling dataset; A direct reconstruction scheme then extracts images of the pharmacokinetic parameters using the adaptive-EKF strategy. We design a dynamic phantom that can simulate the agent metabolism in living tissue. The results of the dynamic phantom experiments verify the validity of the experiment system and reconstruction algorithms, and demonstrate that system provides good resolution, high sensitivity and quantitativeness at different pump speed.
Coupling between transport theory and its diffusion approximation in subdomain-based hybrid models for enhanced description of near-field photon-migration can be computationally complex, or even physically inaccurate. We report on a physically consistent coupling method that links the transport and diffusion physics of the photons according to transient photon kinetics, where distribution of the fully diffusive photons at a transition time is provided by a computation-saving auxiliary time-domain diffusion solution. This serves as a complementary or complete isotropic source of the temporally integrated transport equation over the early stage and the diffusion equation over the late stage, respectively, from which the early and late photodensities can be acquired independently and summed up to achieve steady-state modeling of the whole transport process. The proposed scheme is validated with numerical simulations for a cubic geometry.
KEYWORDS: Fluorescence tomography, Data modeling, Tissues, Signal to noise ratio, Instrument modeling, Performance modeling, In vivo imaging, Digital filtering, Image filtering, Electronic filtering
We present a generalized strategy for direct reconstruction in pharmacokinetic diffuse fluorescence tomography (DFT) with CT-analogous scanning mode, which can accomplish one-step reconstruction of the indocyanine-green pharmacokinetic-rate images within in vivo small animals by incorporating the compartmental kinetic model into an adaptive extended Kalman filtering scheme and using an instantaneous sampling dataset. This scheme, compared with the established indirect and direct methods, eliminates the interim error of the DFT inversion and relaxes the expensive requirement of the instrument for obtaining highly time-resolved date-sets of complete 360 deg projections. The scheme is validated by two-dimensional simulations for the two-compartment model and pilot phantom experiments for the one-compartment model, suggesting that the proposed method can estimate the compartmental concentrations and the pharmacokinetic-rates simultaneously with a fair quantitative and localization accuracy, and is well suitable for cost-effective and dense-sampling instrumentation based on the highly-sensitive photon counting technique.
In a typical laminar optical tomography (LOT) system, the dip-angle between the incident light (or the emitting light) and the normal of the detection plane randomly changes during raster-scanning. The inconstant dip-angle causes consistency between the measurement and the light transportation model where a fixed dip-angle of the incident light is generally required. To eliminate the effect from this dip angle, methods such as keeping the angle unchangeable by moving the phantom instead of scanning the light were investigated. In this paper, a LOT system with small dip-angle over the whole detection range is developed. Simulation and experimental evaluation show that the dip-angle of the modified system is much smaller than that of the traditional system. For example, the relative angle between the two incident light at (x=0mm, y=0mm) and (x=0mm, y=2.5mm) on the image plane is about 0.7° for the traditional system while that is only about 0.02° for the modified system. The main parameters of the system are also evaluated and an image reconstruction algorithm is developed based on Monte Carlo simulation. The reconstructed images show that the spatial resolution and quantitative ratio is improved by the modified system without loss of the scanning speed.
Diffuse optical tomography (DOT) is a biomedical imaging technology for noninvasive visualization of spatial variation
about the optical properties of tissue, which can be applied to in vivo small-animal disease model. However, traditional
DOT suffers low spatial resolution due to tissue scattering. To overcome this intrinsic shortcoming, multi-modal
approaches that incorporate DOT with other imaging techniques have been intensively investigated, where a priori
information provided by the other modalities is normally used to reasonably regularize the inverse problem of DOT.
Nevertheless, these approaches usually consider the anatomical structure, which is different from the optical structure.
Photoacoustic tomography (PAT) is an emerging imaging modality that is particularly useful for visualizing lightabsorbing
structures embedded in soft tissue with higher spatial resolution compared with pure optical imaging. Thus, we
present a PAT-guided DOT approach, to obtain the location a priori information of optical structure provided by PAT
first, and then guide DOT to reconstruct the optical parameters quantitatively. The results of reconstruction of phantom
experiments demonstrate that both quantification and spatial resolution of DOT could be highly improved by the
regularization of feasible-region information provided by PAT.
In diffuse florescence tomography (DFT), the radiative transfer equation (RTE) and its P1 approximation, i.e. the diffuse equation (DE), have been used as the forward models. Since the assumptions of the diffusion approximation are not valid in particular regions of biological tissue which are close to the collimated light sources and boundaries, not scattering dominated or having void-like sub-domains, the RTE-based DFT methodology has become a focus of investigation. Therefore, we present a RTE-based featured-data scheme for time-domain DFT, which combines the discrete solidangle- element method and the finite element method to obtain numerical solutions of the Laplace-transformed 2D timedomain RTE, with the natural boundary condition and collimating light source model. The scheme is validated using the measurement data from phantom and in-vivo small-animal experiments compared to the DE-based scheme.
Shape-parameterized diffuse optical tomography (DOT), which is based on a priori that assumes the uniform distribution
of the optical properties in the each region, shows the effectiveness of complex biological tissue optical heterogeneities
reconstruction. The priori tissue optical structure could be acquired with the assistance of anatomical imaging methods
such as X-ray computed tomography (XCT) which suffers from low-contrast for soft tissues including different optical
characteristic regions. For the mouse model, a feasible strategy of a priori tissue optical structure acquisition is proposed
based on a non-rigid image registration algorithm. During registration, a mapping matrix is calculated to elastically align
the XCT image of reference mouse to the XCT image of target mouse. Applying the matrix to the reference atlas which
is a detailed mesh of organs/tissues in reference mouse, registered atlas can be obtained as the anatomical structure of
target mouse. By assigning the literature published optical parameters of each organ to the corresponding anatomical
structure, optical structure of the target organism can be obtained as a priori information for DOT reconstruction
algorithm. By applying the non-rigid image registration algorithm to a target mouse which is transformed from the
reference mouse, the results show that the minimum correlation coefficient can be improved from 0.2781 (before
registration) to 0.9032 (after fine registration), and the maximum average Euclid distances can be decreased from
12.80mm (before registration) to 1.02mm (after fine registration), which has verified the effectiveness of the algorithm.
Diffuse florescence tomography (DFT) as a high-sensitivity optical molecular imaging tool, can be applied to in vivo
visualize interior cellular and molecular events for small-animal disease model through quantitatively recovering
biodistributions of specific molecular probes. In DFT, the radiative transfer equation (RTE) and its approximation, such
as the diffuse equation (DE), have been used as the forward models. The RTE-based DFT methodology is more suitable
for biological tissue having void-like regions and the near-source area as in the situations of small animal imaging. We
present a RTE-based scheme for the steady state DFT, which combines the discrete solid angle method and the finite
difference method to obtain numerical solutions of the 2D steady RTE, with the natural boundary condition and
collimating light source model. The approach is validated using the forward data from the Monte Carlo simulation for its
better performances in the spatial resolution and reconstruction fidelity compared to the DE-based scheme.
The full time-resolved methods of diffuse fluorescence tomography (DFT) are known to improve image resolution and accuracy significantly. However, these methods usually suffer from low practical efficacy due to the influence of the instrumental response function (IRF) and the tradeoff between the used data time-resolution and the required signal-to-noise ratio (SNR). We herein present a full time-resolved approach that combines an IRF-calibrated time-resolved Born normalization and an overlap-delaying time-gating strategy for attaining high SNR without sacrificing the time-resolved information content. Phantom experiments demonstrate that the approach outperforms the traditional DFT methods in spatial resolution and reconstruction fidelity.
We derive a modification method to simplify the SPN coupled partial differential equations into some independent equations. The modification leads to significant mathematical simplifications and can be used to calculate the Green’s function of the SPN equations for infinite and semi-infinite turbid medium. The obtained analytical solutions depend on eigenvectors and eigenvalues. Compared with the derived methods based on coupled equations, the derivation process of our proposed method is general, fast and simple. The derived analytical solutions are successfully verified by comparisons with Monte Carlo simulations.
A region-based approach of image reconstruction using the finite element method is developed for diffuse optical tomography (DOT). The method is based on the framework of the pixel-based DOT methodology and on an assumption that different anatomical regions have their respective sets of the homogeneous optical properties distributions. With this hypothesis, the region-based DOT solution greatly improves the ill-posedness of the inverse problem by reducing the number of unknowns to be reconstructed. The experimental validation of the methodology is performed on a solid phantom employing a multi-channel DOT system of lock-in photon-counting mode, as well as compared with the traditional pixel-based reconstruction results, demonstrate that the proposed DOT methodology presents a promising tool of in vivo reconstructing background optical structures with the aid of anatomical a priori.
To cope with the low quantification in the established optical topography that originates from the excessively simplified computation model based on the modified Lambert-Beer’s Law (MLBL), we propose a least-squares fitting scheme for time-domain optical topography that seeks for data matching between the time-resolved measurement and the model prediction calculated by analytically solving the time-domain diffusion equation in semi-infinite geometry. Our simulative and phantom experiments demonstrate that the proposed curve-fitting method is overall superior to the conventional MLBL-based one in quantitative performance.
At present, the most widely accepted forward model in diffuse optical tomography (DOT) is the diffusion equation,
which is derived from the radiative transfer equation by employing the P1 approximation. However, due to its validity
restricted to highly scattering regions, this model has several limitations for the whole-body imaging of small-animals,
where some cavity and low scattering areas exist. To overcome the difficulty, we presented a Graphic-Processing-
Unit(GPU) implementation of Monte-Carlo (MC) modeling for photon migration in arbitrarily heterogeneous turbid
medium, and, based on this GPU-accelerated MC forward calculation, developed a fast, universal DOT image
reconstruction algorithm. We experimentally validated the proposed method using a continuous-wave DOT system in the
photon-counting mode and a cylindrical phantom with a cavity inclusion.
The importance of cellular pH has been shown clearly in the study of cell activity, pathological feature, and drug metabolism. Monitoring pH changes of living cells and imaging the regions with abnormal pH-values, in vivo, could provide invaluable physiological and pathological information for the research of the cell biology, pharmacokinetics, diagnostics, and therapeutics of certain diseases such as cancer. Naturally, pH-sensitive fluorescence imaging of bulk tissues has been attracting great attentions from the realm of near infrared diffuse fluorescence tomography (DFT). Herein, the feasibility of quantifying pH-induced fluorescence changes in turbid medium is investigated using a continuous-wave difference-DFT technique that is based on the specifically designed computed tomography-analogous photon counting system and the Born normalized difference image reconstruction scheme. We have validated the methodology using two-dimensional imaging experiments on a small-animal-sized phantom, embedding an inclusion with varying pH-values. The results show that the proposed approach can accurately localize the target with a quantitative resolution to pH-sensitive variation of the fluorescent yield, and might provide a promising alternative method of pH-sensitive fluorescence imaging in addition to the fluorescence-lifetime imaging.
The importance of cellular pH has been shown clearly in the study of cell activity, pathological feature, drug metabolism,
etc. Monitoring pH changes of living cells and imaging the regions with abnormal pH values in vivo could provide the
physiologic and pathologic information for the research of the cell biology, pharmacokinetics, diagnostics and
therapeutics of certain diseases such as cancer. Thus, pH-sensitive fluorescence imaging of bulk tissues has been
attracting great attention in the regime of near-infrared diffuse fluorescence tomography (DFT), an efficient small-animal
imaging tool. In this paper, the feasibility of quantifying pH-sensitive fluorescence targets in turbid medium is
investigated using both time-domain and steady-state DFT methods. By use of the specifically designed time-domain and
continuous-wave systems and the previously proposed image reconstruction scheme, we validate the method through
2-dimensional imaging experiments on a small-animal-sized phantom with multiply targets of distinct pH values. The
results show that the approach can localize the targets with reasonable accuracy and achieve quantitative reconstruction
of the pH-sensitive fluorescent yield.
In vivo biomedical imaging using near-infrared light must overcome the effects of highly light scattering, which limit the
spatial resolution and affect image quality. The high-resolution, sensitive and quantitative fluorescence imaging tool is an
urgent need for the applications in small-animal imaging and clinical cancer research. A CT-analogous method for
fluorescence molecular tomography (FMT) on small-animal-sized models is presented to improve the spatial resolution
of FMT to a limit of several millimeters, depending on the size of the tissue region to be imaged. The method combines
FMT physics with the filtered back-projection scheme for image reconstruction of the fan-beam computed tomography,
based on the early-photon detection of time-resolved optical signals, and is suitable for two-dimensional (2D) imaging of
small size biological models. By use of a normalized Born formulation for the inversion, the algorithm is validated using
full time-resolved simulated data for 2D phantom that are generated from a hybrid finite-element and
finite-time-difference photon diffusion modeling, and its superiority in the improvement of the spatial resolution is
demonstrated by imaging different target-to-background contrast ratios.
Traditionally, volume based finite element method (FEM) or finite difference method (FDM) are applied to the forward
problem of the time-domain diffuse fluorescence tomography (DFT), this paper presents a new numerical method for
solving the problem: the boundary element method (BEM). Using BEM forward solver is explored as an alternative to
the FEM or FDM solution methodology for the elliptic equations used to model the generation and transport of
fluorescent light in highly scattering media. In contrast to the FEM or FDM, the boundary integral method requires only
representation of the surface meshes, thus requires many fewer nodes and elements than the FEM and FDM. By using
BEM forward solver for time-domain DFT, we can simultaneously reconstruct both fluorescent yield and lifetime images.
The results have demonstrated that the BEM is suitable for solving the forward problem of time-domain DFT.
A fiber-based non-contact scheme of the time-domain diffuse fluorescence yield and lifetime tomography is described
that combines the time-correlated single photon counting technique for high-sensitive, time-resolved detection and
CT-analogous configuration for high throughput data collection. A pilot validation of the methodology is performed for
two-dimensional scenarios using simulated and experimental data. The results demonstrated the potential of the proposed
scheme in improving the image quality.
A time domain noncontact fluorescence tomography system and the corresponding reconstruction algorithm towards the
early diagnosis of breast cancer are developed. The time domain system based on the time-correlated single photon
counting technique is adopted to provide both the high sensitivity in detection and good capability in multi-parameter
reconstruction. Comparing to the conventional contact measurement mode, the noncontact system with light scanning
can provide more measurement data for improving the spatial resolution of the images. The performance and efficacy of
the system is evaluated with measurements on solid phantoms. For the phantom with single fluorescent target, the
fluorescence yield and lifetime were simultaneously reconstructed with good quality. For the phantom with two
fluorescent targets, the targets with the center-to-center separation of 20mm and the edge separation of 15mm can be
distinguished. Measurements also show that the reconstructed yields are linear to the concentration of the fluorescence
dye. The results demonstrated the potential of the system in the in vivo diagnosis of the early breast cancer.
Diffuse fluorescence tomography (DFT) provides spatial distributions of fluorescence parameters by measuring
fluorescence signals of probes or agents that are targeted to interior specific molecules or tissues. The potential
applications of DFT can be found in drug development and early tumor diagnosis. This work proposes a CT-analogous
mode of DFT, where the imaging chamber is impinged by collimated beam from a fiber-coupled laser diode and the
resultant fluorescence re-emissions on the opposite side, i.e., the so-called "projections", are collected by eight detection
fibers placed from 101.25º to 258.75º perspectives opposite to the incidence that are then successively filtered out into a
photon-counting channel for quantification. By rotating the imaging chamber or phantom at an angular, the system
acquires the "projections" of surface-emitted fluorescence under different perspectives as a CT system does. This ease of
acquiring a large data-set enables realization of high-quality imaging. Pilot experiments on phantoms with Cy5.5-target
embedded have validated the efficacy of the proposed method.
A novel method for optical breast imaging was presented based on fluorescence guided diffusion optical tomography
(DOT). In this paper, the time-domain fluorescence parameters (yield and lifetime) were reconstructed based on discrete
wavelet transform at first, then the fluorescence images were used to guide and constrain the diffusion optical
tomography reconstruction, and the image segmentation strategy based on wavelet coefficient was applied to improve
the image quality in DOT. To validate the proposed method, the numerical simulation was performed to demonstrate its
computational efficacy. The results showed the feasibility of this method, and the spatial resolution, quantification and
computational efficiency in fluorescence diffusion optical tomography and DOT were enhanced evidently.
In biomedical optics, the Monte Carlo (MC) simulation is widely recognized as a gold standard for its high accuracy and
versatility. However, in fluorescence regime, due to the requirement for tracing a huge number of the consecutive events
of an excitation photon migration, the excitation-to-emission convention and the resultant fluorescent photon migration
in tissue, the MC method is prohibitively time-consuming, especially when the tissue has an optically heterogeneous
structure. To overcome the difficulty, we present a parallel implementation of MC modeling for fluorescence propagation
in tissue, on the basis of the Graphics Processing Units (GPU) and the Compute Unified Device Architecture (CUDA)
platform. By rationalizing the distribution of blocks and threads a certain number of photon migration procedures can be
processed synchronously and efficiently, with the single-instruction-multiple-thread execution mode of GPU. We have
evaluated the implementation for both homogeneous and heterogeneous scenarios by comparing with the conventional
CPU implementations, and shown that the GPU method can obtain significant acceleration of about 20-30 times for
fluorescence modeling in tissue, indicating that the GPU-based fluorescence MC simulation can be a practically effective
tool for methodological investigations of tissue fluorescence spectroscopy and imaging.
Quantitative measurements of fluorescent parameters have merited great interest lately for near-infrared fluorescence
diffuse optical tomography - the efficient small animal imaging tool. We present a two-dimensional image reconstruction
method for time-domain fluorescence diffuse optical tomography, which employs the analytical solution to the
Laplace-transformed time-domain photon-diffusion equation to construct the inverse model and introduces a pair of
real-domain transform-factors to effectively separate the fluorescent yield and lifetime parameters from the algebraic
reconstruction technique solutions to the resultant linear inversions. By use of a specifically designed a multi-channel
time-correlated single photon counting system and a normalized Born formulation for the inversion, the proposed
scheme in a circular domain is experimentally validated using small-animal-sized cylindrical phantoms that embed
several fluorescent targets made from 1%-Intralipid solution and differently contrasting fluorescent agents, where the
time-resolved excitation and fluorescence signals are measured on the boundary. The results show that the approach
retrieves the positions and shapes of the targets with a reasonable accuracy and simultaneously achieve quantitative
reconstruction of the fluorescent yield and lifetime.
We present a scheme for fluorescence guided diffusion optical tomography to reconstruct the fluorescence parameters
(yield and lifetime) and optical parameters (absorption and reduced coefficients) based on time-resolved data. In this
paper, the fluorescence parameters were reconstructed at first, then the fluorescence images were used to guide and
constrain the diffusion optical tomography reconstruction, and the binary image segmentation strategy was applied to
improve the image quality in DOT. To validate the proposed method, the numerical simulation was performed to
demonstrate its computational efficacy. The results showed the feasibility of this method, and the spatial resolution,
quantification and computational efficiency in DOT were enhanced evidently.
Near-infrared fluorescence diffuse optical tomography has proven to be an efficient tool for visualizing the
bio-distributions of fluorescent markers in tissue. We present a two-dimensional image reconstruction method for
time-domain fluorescence diffuse optical tomography on a turbid medium of circular domain. The methodology is based
on a linear generalized pulse spectrum technique that employs the analytical solution to the Laplace-transformed
time-domain photon-diffusion equation to construct a Born normalized inverse model. A pair of real domain
transform-factors is introduced to simultaneously reconstruct the fluorescent yield and lifetime images and the resultant
linear inversions are solved using an algebraic reconstruction technique. The algorithm is validated using simulated data,
and the spatial resolution, noise-robustness and so on are assessed. The experimental validation is performed using a
multi-channel time-correlated single-photon-counting system and a cylinder phantom that embeds a fluorescent target
made from 1%-Intralipid solution and Cy5.5 agent. The results show that the approach retrieves the position and shape of
the target with a reasonable accuracy.
Time-domain fluorescence diffuse optical tomography (FDOT) can provide information, not only concerning the
localization of specific fluorophores, but also about the local fluorophore environment. We present a method based on
linear inversion algorithm to reconstruct images of fluorescence yield and lifetime from time-resolved data. To provide
efficient solutions, we convert the data type by Laplace transform and adapt normalized Born ratio for its advantages in
fluorescence mode. The methodology is experimentally validated in reflection and transmittance measurements by use of
time-correlation single photon counting system. We experimentally validate that the proposed scheme can achieve
simultaneous three-dimensional reconstruction of the fluorescent yield and lifetime. The results show that for the
positions, sizes and shapes of the targets, there are some deviation in reflection measurement, the quality in transmittance
one is more satisfied.
Non-contact scheme is prevalent to diffuse fluorescence tomography (FDT) since it facilitates instrumentation as well as
simplifies experimental procedure. Although non-contact FDT generally uses CCD camera as detectors to achieve high
throughput of data collection, a fiber-based implementation can make full use of well-established high-sensitive and
time-resolved detection techniques. Therefore, a system that combines the fiber-based time-resolved detection and the
non-contact geometry of optodes would be significantly attractive, which also means a more complex modeling of
photon migration. This paper presents detailed computational aspects of the fiber-based non-contact DFT, including both
the forward and inverse models. A pilot validation of the method is performed using simulated data for a
two-dimensional case.
A full three-dimensional, featured-data algorithm for time-domain diffuse fluorescence tomography is presented, which
inverts the Laplace-transformed time-domain coupled diffusion equations and employs a pair of real-domain
transform-factors to effectively separate the fluorescent yield and lifetime parameters. By use of a multi-channel
time-correlation single photon counting system and a normalized Born formulation for the inversion, the proposed
scheme is experimentally validated to achieve simultaneous reconstruction of the fluorescent yield and lifetime
distributions with a reasonable accuracy.
KEYWORDS: Luminescence, Tomography, Fluorescence tomography, Diffusion, Data modeling, Atrial fibrillation, 3D modeling, Reconstruction algorithms, Finite element methods, Single photon
We propose a 3D scheme for time-domain fluorescence molecular tomography within the normalized Born-ratio
formulation. A finite element method solution to the Laplace transformed time-domain coupled diffusion equations is
employed as the forward model, and the resultant linear inversions at two distinct transform-factors are solved with an
algebraic reconstruction technique to separate fluorescent yield and lifetime images. By use of a multichannel
time-correlation single photon counting system, we experimentally validate that the proposed scheme can achieve
simultaneous reconstruction of the fluorescent yield and lifetime distributions with a reasonable accuracy.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.