Image reconstruction in fluorescence molecular tomography with sparsity-initialized maximum-likelihood expectation maximization

We present a reconstruction method involving maximum-likelihood expectation maximization (MLEM) to model Poisson noise as applied to fluorescence molecular tomography (FMT). MLEM is initialized with the output from a sparse reconstruction-based approach, which performs truncated singular value decomposition-based preconditioning followed by fast iterative shrinkage-thresholding algorithm (FISTA) to enforce sparsity. The motivation for this approach is that sparsity information could be accounted for within the initialization, while MLEM would accurately model Poisson noise in the FMT system. Simulation experiments show the proposed method significantly improves images qualitatively and quantitatively. The method results in over 20 times faster convergence compared to uniformly initialized MLEM and improves robustness to noise compared to pure sparse reconstruction. We also theoretically justify the ability of the proposed approach to reduce noise in the background region compared to pure sparse reconstruction. Overall, these results provide strong evidence to model Poisson noise in FMT reconstruction and for application of the proposed reconstruction framework to FMT imaging

Adaptive finite element methods for fluorescence enhanced optical tomography

Fluorescence enhanced optical tomography is a promising molecular imaging modality which employs a near infrared fluorescent molecule as an imaging agent and time-dependent measurements of fluorescent light propagation and generation. In this dissertation a novel fluorescence tomography algorithm is proposed to reconstruct images of targets contrasted by fluorescence within the tissues from boundary fluorescence emission measurements. An adaptive finite element based reconstruction algorithm for high resolution, fluorescence tomography was developed and validated with non-contact, planewave frequency-domain fluorescence measurements on a tissue phantom. The image reconstruction problem was posed as an optimization problem in which the fluorescence optical property map which minimized the difference between the experimentally observed boundary fluorescence and that predicted from the diffusion model was sought. A regularized Gauss-Newton algorithm was derived and dual adaptive meshes were employed for solution of coupled photon diffusion equations and for updating the fluorescence optical property map in the tissue phantom. The algorithm was developed in a continuous function space setting in a mesh independent manner. This allowed the meshes to adapt during the tomography process to yield high resolution images of fluorescent targets and to accurately simulate the light propagation in tissue phantoms from area-illumination. Frequency-domain fluorescence data collected at the illumination surface was used for reconstructing the fluorescence yield distribution in a 512 cm3, tissue phantom filled with 1% Liposyn solution. Fluorescent targets containing 1 micro-molar Indocyanine Green solution in 1% Liposyn and were suspended at the depths of up to 2cm from the illumination surface. Fluorescence measurements at the illumination surface were acquired by a gain-modulated image intensified CCD camera system outfitted with holographic band rejection and optical band pass filters. Excitation light at the phantom surface source was quantified by utilizing cross polarizers. Rayleigh resolution studies to determine the minimum detectable sepatation of two embedded fluorescent targets was attempted and in the absence of measurement noise, resolution down to the transport limit of 1mm was attained. The results of this work demonstrate the feasibility of high-resolution, molecular tomography in clinic with rapid non-contact area measurements

Basis mapping methods for forward and inverse problems

This paper describes a novel method for mapping between basis representation of a field variable over a domain in the context of numerical modelling and inverse problems. In the numerical solution of inverse problems, a continuous scalar or vector field over a domain may be represented in different finite-dimensional basis approximations, such as an unstructured mesh basis for the numerical solution of the forward problem, and a regular grid basis for the representation of the solution of the inverse problem. Mapping between the basis representations is generally lossy, and the objective of the mapping procedure is to minimise the errors incurred. We present in this paper a novel mapping mechanism that is based on a minimisation of the L2 or H1 norm of the difference between the two basis representations. We provide examples of mapping in 2D and 3D problems, between an unstructured mesh basis representative of an FEM approximation, and different types of structured basis including piecewise constant and linear pixel basis, and blob basis as a representation of the inverse basis. A comparison with results from a simple sampling-based mapping algorithm shows the superior performance of the method proposed here

Innovative boundary integral and hybrid methods for diffuse optical imaging

Diffuse Optical Imaging (DOI), the study of the propagation of Near Infra-Red (NIR) light in biological media, is an emerging method in medical imaging. Its state-of-the-art is non-invasive, versatile and reasonably inexpensive. In Diffuse Optical Tomography (DOT), the adaptation of numerical methods such as the Finite Element Method (FEM) and, more recently the Boundary Element Method (BEM), has allowed the treatment of complex problems, even for in vivo functional three-dimensional imaging. This work is the first attempt to combine these two methods in DOT. The BEM-FEM is designed to tackle layered turbid media problems. It focuses on the region of interest by restraining the reconstruction to it. All other regions are treated as piecewise-constant in a surface-integral approach. We validated the model in concentric spheres and found that it compared well with an analytical result. We then performed functional imaging of the neonate’s motor cortex in vivo, in a reconstruction restricted to the brain, both with FEM and BEM-FEM. Another use of the BEM in DOI is also outlined. NIR Spectroscopy (NIRS) devices are particularly used in brain monitoring and Diffuse Optical Cortical Mapping (DOCM). Unfortunately, they are very often accompanied by rudimentary analysis of the data and the 3D appreciation of the problem is missed. The BEM DOCM developed in the current work represents an improvement, especially since a topographical representation of a motor activation in the cortex is clearly reconstructed in vivo. In the interest of computational speed an acceleration technique for the BEM has been developed. The Fast Multipole Method (FMM), which is based on the decomposition of Green’s function on a basis of Bessel and Hankel functions, eases the evaluation of the BEM matrix, along with a faster calculation of the solutions

Advanced tomographic image reconstruction algorithms for Diffuse Optical Imaging

Diffuse Optical Imaging is relatively new set of imaging modality that use infrared and near infrared light to characterize the optical properties of biological tissue. The technology used is less expensive than other imaging modalities such as X-ray mammography, it is portable and can be used to monitor brain activation and cancer diagnosis, besides to aid to other imaging modalities and therapy treatments in the characterization of diseased tissue, i. e. X-ray, Magnetic Resonance Imaging and Radio Frequency Ablation. Due the optical properties of biological tissue near-infrared light is highly scattered, as a consequence, a limited amount of light is propagated thus making the image reconstruction process very challenging. Typically, diffuse optical image reconstructions require from several minutes to hours to produce an accurate image from the interaction of the photons and the chormophores of the studied medium. To this day, this limitation is still under investigation and there are several approaches that are close to the real-time image reconstruction operation. Diffuse Optical Imaging includes a variety of techniques such as functional Near-Infrared Spectroscopy (fNIRS), Diffuse Optical Tomography (DOT), Fluorescence Diffuse Optical Tomography (FDOT) and Spatial Frequency Domain imaging (SFDI). These emerging image reconstruction modalities aim to become routine modalities for clinical applications. Each technique presents their own advantages and limitations, but they have been successfully used in clinical trials such as brain activation analysis and breast cancer diagnosis by mapping the response of the vascularity within the tissue through the use of models that relate the interaction between the tissue and the path followed by the photons. One way to perform the image reconstruction process is by separating it in two stages: the forward problem and the inverse problem; the former is used to describe light propagation inside a medium and the latter is related to the reconstruction of the spatio-temporal distribution of the photons through the tissue. Iterative methods are used to solve both problems but the intrinsic complexity of photon transport in biological tissue makes the problem time-consuming and computationally expensive. The aim of this research is to apply a fast-forward solver based on reduced order models to Fluorescence Diffuse Optical Tomography and Spatial Frequency Domain Imaging to contribute to these modalities in their application of clinical trials. Previous work showed the capabilities of the reduced order models for real-time reconstruction of the absorption parameters in the brain of mice. Results demonstrated insignificant loss of quantitative and qualitative accuracy and the reconstruction was performed in a fraction of the time normally required on this kind of studies. The forward models proposed in this work, offer the capability to run three-dimensional image reconstructions in CPU-based computational systems in a fraction of the time required by image reconstructions methods that use meshes generated using the Finite Element Method. In the case of SFMI, the proposed approach is fused with the approach of the virtual sensor for CCD cameras to reduce the computational burden and to generate a three-dimensional map of the distribution of tissue optical properties. In this work, the use case of FDOT focused on the thorax of a mouse model with tumors in the lungs as the medium under investigation. The mouse model was studied under two- and three- dimension conditions. The two-dimensional case is presented to explain the process of creating the Reduced-Order Models. In this case, there is not a significant improvement in the reconstruction considering NIRFAST as the reference. The proposed approach reduced the reconstruction time to a quarter of the time required by NIRFAST, but the last one performed it in a couple of seconds. In contrast, the three-dimensional case exploited the capabilities of the Reduced-Order Models by reducing the time of the reconstruction from a couple of hours to several seconds, thus allowing a closer real-time reconstruction of the fluorescent properties of the interrogated medium. In the case of Spatial Frequency Domain Imaging, the use case considered a three-dimensional section of a human head that is analysed using a CCD camera and a spatially modulated light source that illuminates the mentioned head section. Using the principle of the virtual sensor, different regions of the CCD camera are clustered and then Reduced Order Models are generated to perform the image reconstruction of the absorption distribution in a fraction of the time required by the algorithm implemented on NIRFAST. The ultimate goal of this research is to contribute to the field of Diffuse Optical Imaging and propose an alternative solution to be used in the reconstruction process to those models already used in three-dimensional reconstructions of Fluorescence Diffuse Optical Tomography and Spatial Frequency Domain Imaging, thus offering the possibility to continuously monitor tissue obtaining results in a matter of seconds

Fluorescence enhanced optical tomography on breast phantoms with measurements using a gain modulated intensified CCD imaging system

Fluorescence-enhanced optical imaging using near-infrared (NIR) light developed for in-vivo molecular targeting and reporting of cancer provides promising opportunities for diagnostic imaging. However, prior to the administration of unproven contrast agents, the benefits of fluorescence-enhanced optical imaging must be assessed in feasibility phantom studies. A novel intensified charge-coupled device (ICCD) imaging system has been developed to perform 3-D fluorescence tomographic imaging in the frequency-domain using near-infrared contrast agents. This study is unique since it (i) employs a large tissue-mimicking phantom (~1087 cc), which is shaped and sized to resemble a female breast and part of the extended chest wall region, and (ii) enables rapid data acquisition in the frequency-domain by using a gain-modulated ICCD camera. Diagnostic 3-D fluorescence-enhanced optical tomography is demonstrated using 0.5-1 cc single and multiple targets contrasted from their surrounding by ??M concentrations of Indocyanine green (ICG) in the breast-shaped phantom (10 cm diameter), under varying conditions of target-to-background absorption contrast ratios (1:0 and 100:1) and target depths (up to 3 cm deep). Boundary surface fluorescence measurements of referenced amplitude and phase shift were used along with the coupled diffusion equation of light propagation in order to perform 3-D image reconstructions using the approximate extended Kalman filter (AEKF) algorithm, and hence differentiate the target from the background based on fluorescent optical contrast. Detection of single and multiple targets is demonstrated under various conditions of target depths (up to 2 cm deep), absorption optical contrast ratio (1:0 and 100:1), target volumes (0.5-1 cc), and multiple targets (up to three 0.5 cc targets). The feasibility of 3-D image reconstructions from simultaneous multiple point excitation sources are presented. Preliminary lifetime imaging studies with 1:2 and 2:1 optical contrast in fluorescence lifetime of the contrast agents is also demonstrated. The specificity of the optical imager is further assessed from homogeneous phantom studies containing no fluorescently contrasted targets. While nuclear imaging currently provides clinical diagnostic opportunities using radioactive tracers, molecular targeting of tumors using non-ionizing NIR contrast agents tomographically imaged using the frequency-domain ICCD imaging system could possibly become a new method of diagnostic imaging