Fluorescence Diffuse Optical Tomography Research Articles

Fluorescence diffuse optical monitoring of bioreactors: a hybrid deep learning and model-based approach for tomography.

Biosynthesis in bioreactors plays a vital role in many applications, but tools for accurate in situ monitoring of the cells are still lacking. By engineering the cells such that their conditions are reported through fluorescence, it is possible to fill in the gap using fluorescence diffuse optical tomography (fDOT). However, the spatial accuracy of the reconstruction can still be limited, due to e.g. undersampling and inaccurate estimation of the optical properties. Utilizing controlled phantom studies, we use a two-step hybrid approach, where a preliminary fDOT result is first obtained using the classic model-based optimization, and then enhanced using a neural network. We show in this paper using both simulated and phantom experiments that the proposed method can lead to a 8-fold improvement (Intersection over Union) of fluorescence inclusion reconstruction in noisy conditions, at the same speed of conventional neural network-based methods. This is an important step towards our ultimate goal of fDOT monitoring of bioreactors.

Local analysis for locating a single point target in time-domain fluorescence diffuse optical tomography

Local analysis for locating a single point target in time-domain fluorescence diffuse optical tomography

Deep background-mismodeling-learned reconstruction for high-accuracy fluorescence diffuse optical tomography.

We present a deep background-mismodeling-learned reconstruction framework for high-accuracy fluorescence diffuse optical tomography (FDOT). A learnable regularizer incorporating background mismodeling is formulated in the form of certain mathematical constraints. The regularizer is then learned to obtain the background mismodeling automatically using a physics-informed deep network implicitly. Here, a deep-unrolled FIST-Net for optimizing L1-FDOT is specially designed to obtain fewer learning parameters. Experiments show that the accuracy of FDOT is significantly improved via implicitly learning the background mismodeling, which proves the validity of the deep background-mismodeling-learned reconstruction. The proposed framework can also be used as a general method to improve a class of image modalities based on linear inverse problems with unknown background modeling errors.

Approximate peak time and its application to time-domain fluorescence diffuse optical tomography

Approximate peak time and its application to time-domain fluorescence diffuse optical tomography

Algorithms and instrumentation for rapid spatial frequency domain fluorescence diffuse optical imaging.

Rapid estimation of the depth and margins of fluorescence targets buried below the tissue surface could improve upon current image-guided surgery techniques for tumor resection. We describe algorithms and instrumentation that permit rapid estimation of the depth and transverse margins of fluorescence target(s) in turbid media; the work aims to introduce, experimentally demonstrate, and characterize the methodology. Spatial frequency domain fluorescence diffuse optical tomography (SFD-FDOT) technique is adapted for rapid and computationally inexpensive estimation of fluorophore target depth and lateral margins. The algorithm utilizes the variation of diffuse fluorescence intensity with respect to spatial-modulation-frequency to compute target depth. The lateral margins are determined via analytical inversion of the data using depth information obtained from the first step. We characterize method performance using fluorescent contrast targets embedded in tissue-simulating phantoms. Single and multiple targets with significant lateral size were imaged at varying depths as deep as 1cm. Phantom data analysis showed good depth-sensitivity, and the reconstructed transverse margins were mostly within ∼30 % error from true margins. The study suggests that the rapid SFD-FDOT approach could be useful in resection surgery and, more broadly, as a first step in more rigorous SFD-FDOT reconstructions. The experiments permit evaluation of current limitations.

Uniqueness and numerical inversion in the time-domain fluorescence diffuse optical tomography

This work considers the time-domain fluorescence diffuse optical tomography (FDOT). We recover the distribution of fluorophores in biological tissue by the boundary measurements. With the Laplace transform and the knowledge of complex analysis, we build the uniqueness theorem of this inverse problem. After that, the numerical inversions are considered. We introduce an iterative inversion algorithm under the framework of regularizing scheme, then give several numerical examples in three-dimensional space illustrating the performance of the proposed inversion schemes.

Interpretable model-driven projected gradient descent network for high-quality fDOT reconstruction.

In fluorescence diffuse optical tomography (fDOT), the quality of reconstruction is severely limited by mismodeling and ill-posedness of inverse problems. Although data-driven deep learning methods improve the quality of image reconstruction, the network architecture lacks interpretability and requires a lot of data for training. We propose an interpretable model-driven projected gradient descent network (MPGD-Net) to improve the quality of fDOT reconstruction using only a few training samples. MPGD-Net unfolds projected gradient descent into a novel deep network architecture that is naturally interpretable. Simulation and in vivo experiments show that MPGD-Net greatly improves the fDOT reconstruction quality with superior generalization ability.

Indocyanine Green-Camptothecin Co-Loaded Perfluorocarbon Double-Layer Nanocomposite: A Versatile Nanotheranostics for Photochemotherapy and FDOT Diagnosis of Breast Cancer.

Breast cancer remains the most frequently diagnosed cancer and is the leading cause of neoplastic disease burden for females worldwide, suggesting that effective therapeutic and/or diagnostic strategies are still urgently needed. In this study, a type of indocyanine green (ICG) and camptothecin (CPT) co-loaded perfluorocarbon double-layer nanocomposite named ICPNC was developed for detection and photochemotherapy of breast cancer. The ICPNCs were designed to be surface modifiable for on-demand cell targeting and can serve as contrast agents for fluorescence diffuse optical tomography (FDOT). Upon near infrared (NIR) irradiation, the ICPNCs can generate a significantly increased production of singlet oxygen compared to free ICG, and offer a comparable cytotoxicity with reduced chemo-drug dosage. Based on the results of animal study, we further demonstrated that the ICPNCs ([ICG]/[CPT] = 40-/7.5-μM) in association with 1-min NIR irradiation (808 nm, 6 W/cm2) can provide an exceptional anticancer effect to the MDA-MB-231 tumor-bearing mice whereby the tumor size was significantly reduced by 80% with neither organ damage nor systemic toxicity after a 21-day treatment. Given a number of aforementioned merits, we anticipate that the developed ICPNC is a versatile theranostic nanoagent which is highly promising to be used in the clinic.

Focused x-ray luminescence imaging system for small animals based on a rotary gantry.

.Significance: The ability to detect and localize specific molecules through tissue is important for elucidating the molecular basis of disease and treatment. Unfortunately, most current molecular imaging tools in tissue either lack high spatial resolution (e.g., diffuse optical fluorescence tomography or positron emission tomography) or lack molecular sensitivity (e.g., micro-computed tomography, ). X-ray luminescence imaging emerged about 10 years ago to address this issue by combining the molecular sensitivity of optical probes with the high spatial resolution of x-ray imaging through tissue. In particular, x-ray luminescence computed tomography (XLCT) has been demonstrated as a powerful technique for the high-resolution imaging of deeply embedded contrast agents in three dimensions (3D) for small-animal imaging.Aim: To facilitate the translation of XLCT for small-animal imaging, we have designed and built a small-animal dedicated focused x-ray luminescence tomography (FXLT) scanner with a scanner, synthesized bright and biocompatible nanophosphors as contrast agents, and have developed a deep-learning-based reconstruction algorithm.Approach: The proposed FXLT imaging system was designed using computer-aided design software and built according to specifications. nanophosphors doped with europium or terbium were synthesized with a silica shell for increased biocompatibility and functionalized with biotin. A deep-learning-based XLCT image reconstruction was also developed based on the residual neural network as a data synthesis method of projection views from few-view data to enhance the reconstructed image quality.Results: We have built the FXLT scanner for small-animal imaging based on a rotational gantry. With all major imaging components mounted, the motor controlling the gantry can be used to rotate the system with a high accuracy. The synthesized nanophosphors displayed distinct x-ray luminescence emission, which enables multi-color imaging, and has successfully been bound to streptavidin-coated substrates. Lastly, numerical simulations using the proposed deep-learning-based reconstruction algorithm has demonstrated a clear enhancement in the reconstructed image quality.Conclusions: The designed FXLT scanner, synthesized nanophosphors, and deep-learning-based reconstruction algorithm show great potential for the high-resolution molecular imaging of small animals.

Mapping fluorescence resonance energy transfer parameters of a bifunctional agent using time-domain fluorescence diffuse optical tomography.

We present a method to map fluorescence resonance energy transfer (FRET) parameters of a bifunctional photodynamic therapy agent, (2-[1-hexyloxyethyl]-2-devinyl pyropheophorbide-a)-cyanine dye (HPPH-CD) conjugate, which consists of a photosensitizer (HPPH) and a fluorescent agent CD. We utilized time-domain fluorescence diffuse optical tomography, the normalized Born ratio model in the Fourier-domain, and an iterative algorithm to map depth-resolved spatial heterogeneities of FRET parameters. Our results exhibited depth-resolved changes of fluorophore's lifetime and the distance maps due to FRET between HPPH and CD. Our model suggests a potential approach of using FRET parameters to monitor efficacies of multifunctional photodynamic therapy agents in deep tissue.

An efficient lattice Boltzmann method for fluorescent diffuse optical tomography on GPUs

Fluorescent diffuse optical tomography (FDOT) is an emerging imaging modality, with great prospects in areas such as biology and medicine. However, current FDOT encounters difficulty in simulating photon propagation in biological tissue, i.e., the forward problem, which limits its further application in biomedical research. This paper presents a lattice Boltzmann method (LBM) on the GPU to greatly improve the computational efficiency in the forward problem realization. This method separated the LBM simulating the propagation of photon in tissues into collision, streaming and boundary processing processes on GPUs, which are local computational processes and inefficient on CPU, so that we can perform the LBM efficiently. Both the numerical phantom and the physical phantom experiments were carried out to evaluate the performance of the proposed method. The experimental results showed that the proposed method achieved the best performance of 2471 mega lattice-updates per second (MLUPS) and a 118-fold speedup under the precondition of simulation accuracy, compared to the diffusion equation implemented by finite element method (FEM) on CPU. Thus, the LBM on the GPU has the potential for efficiently solving the forward problem in FDOT.

Small-animal 360-deg fluorescence diffuse optical tomography using structural prior information from ultrasound imaging

.We demonstrate dual modality of free-space fluorescence diffuse optical tomography (FDOT) and handheld ultrasound (US) imaging to reveal both functional and structural information in small animals. FDOT is a noninvasive method for examining the fluorophore inside an object from the light distribution of the surface. In FDOT, a 660-nm continuous wave diode laser was used as an excitation source and an electron-multiplying charge-coupled device (EMCCD) was used for fluorescence data acquisition. Both the laser and EMCCD were mounted on a 360-deg rotation gantry for the transmission optical data collection. The structural information is obtained from a 6- to 17-MHz handheld US linear transducer by single-side access and conducts in the reconstruction as soft priors. The rotation ranges from 0 deg to 360 deg; different rotation degrees, object positions, and parameters were determined for comparison. Both phantom and tissue phantom results demonstrate that fluorophore distribution can be recovered accurately and quantitatively using this imaging system. Finally, an animal study confirms that the system can extract a dual-modality image, validating its feasibility for further in vivo experiments. In all experiments, the error and standard deviation decrease as the rotation degree is increased and the error was reduced to 10% when the rotation degree was increased over 135 deg.

A Method for Fluorescent Diffuse Optical Tomography Based on Lattice Boltzmann Forward Model on GPU Parallelization

Fluorescent Diffuse Optical Tomography (FDOT) is an emerging imaging method with great prospects in fields of biology and medicine. However, the current solutions to the forward problem in FDOT are time consuming, which greatly limit the application. We proposed a method for FDOT based on Lattice Boltzmann forward model on GPU to greatly improve the computational efficiency. The Lattice Boltzmann Method (LBM) was used to construct the optical transmission model. This method separated the LBM into collision, streaming and boundary processing processes on GPUs to perform the LBM efficiently, which were local computational and inefficient on CPU. The feasibility of the proposed method was verified by the numerical phantom and the physical phantom experiments. The experimental results showed that the proposed method achieved the best performance of a 118-fold speed up under the precondition of simulation accuracy, comparing to the diffusion equation implemented by Finite Element Method (FEM) on CPU. Thus, the LBM on the GPU may efficiently solve the forward problem in FDOT.

Fast and robust reconstruction algorithm for fluorescence diffuse optical tomography assuming a cuboid target.

A fast algorithm for fluorescence diffuse optical tomography is proposed. The algorithm is robust against the choice of initial guesses. We estimate the position of a fluorescent target by assuming a cuboid (rectangular parallelepiped) for the fluorophore target. The proposed numerical algorithm is verified by a numerical experiment and an experiment with a meat phantom. The target position is reconstructed with a cuboid from measurements in the time domain. Moreover, the long-time behavior of the emission light is investigated making use of the analytical solution to the diffusion equation.

A Fluorescence Diffusion Optical Tomography System Based on Lattice Boltzmann Forward Model

Fluorescence Diffuse Optical Tomography (FDOT) is significant for biomedical applications, such as medical diagnostics, drug research. The fluorescence probe distribution in biological tissues can be quantitatively and non-invasively obtained via FDOT, achieving targets positioning and detection. In order to reduce the cost of FDOT, this study designs a FDOT system based on Lattice Boltzmann forward model. The system is used to realize two functions of light propagation simulation and FDOT reconstruction, and is composed of a parameter module, an algorithm module, a result display module and a data interaction module. In order to verify the effectiveness of the platform, this study carries out the light propagation simulation experiment and the FDOT reconstruction experiment, respectively comparing the Monte Carlo (MC) light propagation simulation results and the real position of the light source to be reconstructed. Experiments show that the proposed FDOT system has good reliability and has a high promotion value.

Noninvasive monitoring of liver metastasis development via combined multispectral photoacoustic imaging and fluorescence diffuse optical tomography.

Rationale: In vivo molecular imaging in preclinical animal models is a tool of choice for understanding the pathophysiological mechanisms involved in cancer development and for conducting drug development research. Moreover, combining several imaging modalities can provide multifaceted, complementary and cross-validated information. Photoacoustic imaging (PAI) is a promising imaging modality that can reflect blood vasculature and tissue oxygenation as well as detect exogenous molecules, but one shortcoming of PAI is a lack of organic photoacoustic contrast agents capable of providing tumor contrast.Methods: In the present study, we designed an animal model of liver metastases from colon cancer and monitored metastasis development by in vivo bioluminescence and X-ray microcomputed tomography. Contrast-agent-free PAI was used to detect the respective amounts of oxy- and deoxyhemoglobin and, thus, liver tissue oxygenation. two contrast agents, Angiostamp800 and indocyanin green (ICG), respectively with and without tumor targeting specificity, were then evaluated for their dual fluorescence and photoacoustic detectability and were then used for combined PAI and fluorescence diffuse optical tomography (fDOT) at various disease development stages.Findings: Contrast-agent-free PAI reflected tumor angiogenesis and gradual hypoxia during metastasis development. Multispectral PAI enabled noninvasive real-time monitoring of ICG blood pharmacokinetics, which demonstrated tumor-related liver dysfunction. Both PAI and fluorescence ICG signals were clearly modified in metastasis-bearing livers but did not allow for differentiation between different disease stages. In contrast, there was a significant improvement achieved by using the tumor-specific marker Angiostamp800, which provided gradually increasing PAI and fDOT signals during metastasis development.Conclusion: We demonstrated for the first time the value of using Angiostamp800 as a bimodal tumor-targeting contrast agent for combined PAI and fluorescence imaging of liver metastasis progression in vivo.

A Lattice Boltzmann Forward Model for Fast Fluorescence Diffuse Optical Tomography

Fluorescent Diffuse Optical Tomography (FDOT), as a new imaging technology, can achieve three-dimensional quantitative functional imaging of probe in biological tissues, and has wide application value in biomedicine. Forward model which describes the photon propagation within a biological tissue is a prerequisite for implementing FDOT and determines the performance of FDOT. To further improve the efficiency of FDOT, this paper proposes a new forward model based on the Lattice Boltzmann (LB) method derived from the discretization of radiation transfer equation and applies it to FDOT. The experimental results of numerical simulation and physical phantom show that the LB-based forward model proposed in this paper can increase the imaging speed of FDOT by about 5 times compared with the traditional diffusion equation method, without reducing its imaging quality.

Three-Dimensional Fluorescence Diffuse Optical Tomography Using the Adaptive Spatial Prior Approach

Traditionally, the reconstruction of fluorescence diffuse optical tomography (FDOT) images requires anatomical images as prior information. To integrate anatomical information about tumors or soft tissues into FDOT, additional equipment and procedures are required, involving co-registration, contrast-agent injection, and alignment. Herein, we propose the adaptive spatial prior approach for reconstructing FDOT images, implemented by using an initial guess of the tumor size and location for single and multi-tumor cases. This technique is most appropriately used when FDOT is combined with limited structural information or a different structural imaging system. This method iteratively modifies the location starting from the estimated location. We conduct simulations, phantom experiments and experiments on meat tissue using the adaptive spatial prior method. The results of these studies demonstrate the feasibility of the adaptive spatial prior approach for FDOT imaging.

Fluorescence molecular imaging based on the adjoint radiative transport equation

A new reconstruction algorithm for fluorescence diffuse optical tomography of biological tissues is proposed. The radiative transport equation in the frequency domain is used to model light propagation. The adjoint method studied in this work provides an efficient way for solving the inverse problem. The methodology is applied to a 2D tissue-like phantom subjected to a collimated laser beam. Indocyanine Green is used as fluorophore. Reconstructed images of the spatial fluorophore absorption distribution is assessed taking into account the residual fluorescence in the medium. We show that illuminating the tissue surface from a collimated centered direction near the inclusion gaves a better reconstruction quality. Two closely positioned inclusions can be accurately localized. Additionally, their fluorophore absorption coefficients can be quantified. However, the algorithm failes to reconstruct smaller or deeper inclusions. This is due to light attenuation in the medium. Reconstructions with noisy data are also achieved with a reasonable accuracy.

A fast forward solver of fluorescence diffuse optical tomography based on the lattice Boltzmann method.

Fluorescence diffuse optical tomography (FDOT) is a new molecular imaging technology, which uses near-infrared light to excite the fluorophore in tissues. According to the measurements detected on the surface of imaged object, the fluorescent quantum yield as well as lifetime of the fluorescence can be reconstructed. However, the reconstruction of FDOT remains a challenging problem because the conventional forward solvers are time consuming. Thus, a forward model solver that would enable the fast imaging is necessary. This paper describes a new forward solver to simulate the propagation of photons in tissues based on the lattice Boltzmann method (LBM). This is accomplished by propagation photons in tissues guided by the LBM. To evaluate the performance of the proposed LBM, based on the numerical simulation, we compared the light distribution generated by the LBM with the diffusion equation implemented by COMSOL in four different cases. The experimental results indicate that compared to diffusion equation, the LBM can reduce the computation time for the forward solver of FDOT while preserving the similar accuracy.