A Small Animal Optical Tomographic Imaging System with Omni-Directional, Non-Contact, Angular-Resolved Fluorescence Measurement Capabilities

The overall goal of this thesis is to develop a new non-contact, whole-body, fluorescence molecular tomography system for small animal imaging. Over the past decade, small animal in vivo imaging has led to a better understanding of many human diseases and improved our ability to develop and test new drugs and medical compounds. Among various imaging modalities, optical imaging techniques have emerged as important tools. In particular, fluorescence and bioluminescence imaging systems have opened new ways for visualizing many molecular pathways inside living animals including gene expression and protein functions. While substantial progress has been made in available prototype and commercial optical imaging systems, there still exist areas for further improvement in the outcome of existing instrumentations. Currently, most small animal optical imaging systems rely on 2D planar imaging that provides limited ability to accurately locate lesions deep inside an animal. Furthermore, most existing tomographic imaging systems use a diffusion model of light propagation, which is of limited accuracy. While more accurate models using the equation of radiative transfer have become available, they have not been widely applied to small animal imaging yet. To overcome the limitations of existing optical small animal imaging systems, a novel imaging system that makes use of the latest hardware and software advances in the field was developed. At the heart of the system is a new double-conical-mirror-based imaging head that enables a single fixed position camera to capture multi-directional views simultaneously. Therefore, the imaging head provides 360-degree measurement data from an entire animal surface in one step. Another benefit provided by this design is the substantial reduction of multiple back-reflections between the animal and mirror surfaces. These back reflections are common in existing mirror-based imaging heads and tend to degrade the quality of raw measurement data. Furthermore, the conical-mirror design offers the capability to measure angular-resolved data from the animal surface. To make full use of this capability, a novel equation of radiative transfer-based ray-transfer operator was introduced to map the spatial and angular information of emitted light on the animal surface to the captured image data. As a result, more data points are involved into the image reconstructions, which leads to a higher image resolution. The performance of the imaging system was evaluated through numerical simulations, experiments using a well-defined tissue phantom, and live-animal studies. Finally, the double reflection mirror scheme presented in this dissertation can be cost-effectively employed with all camera-based imaging systems. The shapes and sizes of mirrors can be varied to accommodate imaging of other objects such as larger animals or human body parts, such as the breast, head, or feet

Mathematical Methods in Tomography

This is the seventh Oberwolfach conference on the mathematics of tomography, the first one taking place in 1980. Tomography is the most popular of a series of medical and scientific imaging techniques that have been developed since the mid seventies of the last century

Fast Radiative-Transfer-Equation-Based Image Reconstruction Algorithms for Non-Contact Diffuse Optical Tomography Systems

It is well known that the radiative transfer equation (RTE) is the most accurate deterministic light propagation model employed in diffuse optical tomography (DOT). RTE-based algorithms provide more accurate tomographic results than codes that rely on the diffusion equation (DE), which is an approximation to the RTE, in scattering dominant media. However, RTE based DOT (RTE-DOT) has limited utility in practice due to its high computational cost and lack of support for general non-contact imaging systems. In this dissertation, I developed fast reconstruction algorithms for RTE-based DOT (RTE-DOT), which consists of three independent components: an efficient linear solver for forward problems, an improved optimization solver for inverse problem, and the first light propagation model in free space that fully considers the angular dependency, which can provide a suitable measurement operator for RTE-DOT. This algorithm is validated and evaluated with numerical experiments and clinical data. According to these studies, the novel reconstruction algorithm is up to 30 times faster than traditional reconstruction techniques, while achieving comparable reconstruction accuracy

Use of prior information and probabilistic image reconstruction for optical tomographic imaging

Preclinical bioluminescence tomographic reconstruction is underdetermined. This work addresses the use of prior information in bioluminescence tomography to improve image acquisition, reconstruction, and analysis. A structured light surface metrology method was developed to measure surface geometry and enable robust and automatic integration of mirrors into the measurement process. A mouse phantom was imaged and accuracy was measured at 0.2mm with excellent surface coverage. A sparsity-regularised reconstruction algorithm was developed to use instrument noise statistics to automatically determine the stopping point of reconstruction. It was applied to in silico and in simulacra data and successfully reconstructed and resolved two separate luminescent sources within a plastic mouse phantom. A Bayesian framework was constructed that incorporated bioluminescence properties and instrument properties. Distribution expectations and standard deviations were estimated, providing reconstructions and measures of reconstruction uncertainty. The reconstructions showed superior performance when applied to in simulacra data compared to the sparsity-based algorithm. The information content of measurements using different sets of wavelengths was quantified using the Bayesian framework via mutual information and applied to an in silico problem. Significant differences in information content were observed and comparison against a condition number-based approach indicated subtly different results

Mathematics and Algorithms in Tomography

This was the ninth Oberwolfach conference on the mathematics of tomography. Modalities represented at the workshop included X-ray tomography, radar, seismic imaging, ultrasound, electron microscopy, impedance imaging, photoacoustic tomography, elastography, emission tomography, X-ray CT, and vector tomography along with a wide range of mathematical analysis

Objective assessment of image quality (OAIQ) in fluorescence-enhanced optical imaging

The statistical evaluation of molecular imaging approaches for detecting, diagnosing, and monitoring molecular response to treatment are required prior to their adoption. The assessment of fluorescence-enhanced optical imaging is particularly challenging since neither instrument nor agent has been established. Small animal imaging does not address the depth of penetration issues adequately and the risk of administering molecular optical imaging agents into patients remains unknown. Herein, we focus upon the development of a framework for OAIQ which includes a lumpy-object model to simulate natural anatomical tissue structure as well as the non-specific distribution of fluorescent contrast agents. This work is required for adoption of fluorescence-enhanced optical imaging in the clinic. Herein, the imaging system is simulated by the diffusion approximation of the time-dependent radiative transfer equation, which describes near infra-red light propagation through clinically relevant volumes. We predict the time-dependent light propagation within a 200 cc breast interrogated with 25 points of excitation illumination and 128 points of fluorescent light collection. We simulate the fluorescence generation from Cardio-Green at tissue target concentrations of 1, 0.5, and 0.25 µM with backgrounds containing 0.01 µM. The fluorescence boundary measurements for 1 cc spherical targets simulated within lumpy backgrounds of (i) endogenous optical properties (absorption and scattering), as well as (ii) exogenous fluorophore crosssection are generated with lump strength varying up to 100% of the average background. The imaging data are then used to validate a PMBF/CONTN tomographic reconstruction algorithm. Our results show that the image recovery is sensitive to the heterogeneous background structures. Further analysis on the imaging data by a Hotelling observer affirms that the detection capability of the imaging system is adversely affected by the presence of heterogeneous background structures. The above issue is also addressed using the human-observer studies wherein multiple cases of randomly located targets superimposed on random heterogeneous backgrounds are used in a “double-blind” situation. The results of this study show consistency with the outcome of above mentioned analyses. Finally, the Hotelling observer’s analysis is used to demonstrate (i) the inverse correlation between detectability and target depth, and (ii) the plateauing of detectability with improved excitation light rejection

Improved Modeling and Image Generation for Fluorescence Molecular Tomography (FMT) and Positron Emission Tomography (PET)

In this thesis, we aim to improve quantitative medical imaging with advanced image generation algorithms. We focus on two specific imaging modalities: fluorescence molecular tomography (FMT) and positron emission tomography (PET). For FMT, we present a novel photon propagation model for its forward model, and in addition, we propose and investigate a reconstruction algorithm for its inverse problem. In the first part, we develop a novel Neumann-series-based radiative transfer equation (RTE) that incorporates reflection boundary conditions in the model. In addition, we propose a novel reconstruction technique for diffuse optical imaging that incorporates this Neumann-series-based RTE as forward model. The proposed model is assessed using a simulated 3D diffuse optical imaging setup, and the results demonstrate the importance of considering photon reflection at boundaries when performing photon propagation modeling. In the second part, we propose a statistical reconstruction algorithm for FMT. The algorithm is based on sparsity-initialized maximum-likelihood expectation maximization (MLEM), taking into account the Poisson nature of data in FMT and the sparse nature of images. The proposed method is compared with a pure sparse reconstruction method as well as a uniform-initialized MLEM reconstruction method. Results indicate the proposed method is more robust to noise and shows improved qualitative and quantitative performance. For PET, we present an MRI-guided partial volume correction algorithm for brain imaging, aiming to recover qualitative and quantitative loss due to the limited resolution of PET system, while keeping image noise at a low level. The proposed method is based on an iterative deconvolution model with regularization using parallel level sets. A non-smooth optimization algorithm is developed so that the proposed method can be feasibly applied for 3D images and avoid additional blurring caused by conventional smooth optimization process. We evaluate the proposed method using both simulation data and in vivo human data collected from the Baltimore Longitudinal Study of Aging (BLSA). Our proposed method is shown to generate images with reduced noise and improved structure details, as well as increased number of statistically significant voxels in study of aging. Results demonstrate our method has promise to provide superior performance in clinical imaging scenarios

Reconstruction d’image en fluorescence par tomographie optique diffuse pour imagerie moléculaire sur petit animal avec lumière proche infrarouge en régime continu

L’approximation par harmoniques sphériques (SPN) simplifiées de l’équation de transfert radiatif a été proposée comme un modèle fiable de propagation de la lumière dans les tissus biologiques. Cependant, peu de solutions analytiques ont été trouvées pour ce modèle. De telles solutions analytiques sont d’une grande valeur pour valider les solutions numériques des équations SPN, auxquelles il faut recourir dans le cas de tissus avec des géométries courbes complexes. Dans la première partie de cette thèse, des solutions analytiques pour deux géométries courbes sont présentées pour la première fois, à savoir pour la sphère et pour le cylindre. Pour les deux solutions, les conditions aux frontières générales tenant compte du saut d’indice de réfraction à l’interface du tissus et de son milieu environnant, telles qu’applicables à l’optique biomédicale, sont utilisées. Ces solutions sont validées à l’aide de simulations Monte Carlo basées sur un maillage de discrétisation du milieu. Ainsi, ces solutions permettent de valider rapidement un code numérique, par exemple utilisant les différences finies ou les éléments finis, sans nécessiter de longues simulations Monte Carlo. Dans la deuxième partie de cette thèse, la reconstruction itérative pour l’imagerie par tomographie optique diffuse par fluorescence est proposée sur la base d’une fonction objective et de son terme de régularisation de type Lq-Lp. Pour résoudre le problème inverse d’imagerie, la discrétisation du modèle de propagation de la lumière est effectuée en utilisant la méthode des différences finies. La reconstruction est effectuée sur un modèle de souris numérique en utilisant un maillage multi-échelle. Le problème inverse est résolu itérativement en utilisant une méthode d’optimisation. Pour cela, le gradient de la fonction de coût par rapport à la carte de concentration de l’agent fluorescent est nécessaire. Ce gradient est calculé à l’aide d’une méthode adjointe. Des mesures quantitatives utilisées en l’imagerie médicale sont utilisées pour évaluer la performance de l’approche de reconstruction dans différentes conditions. L’approche Lq-Lp montre des performances quantifiées élevées par rapport aux algorithmes traditionnels basés sur des fonction coût de type somme de carrés de différences.Abstract : The simplified spherical harmonics (SPN) approximation to the radiative transfer equation has been proposed as a reliable model of light propagation in biological tissues. However, few analytical solutions have been found for this model. Such analytical solutions are of great value to validate numerical solutions of the SPN equations, which must be resorted to when dealing with media with complex curved geometries. In the first part of this thesis, analytical solutions for two curved geometries are presented for the first time, namely for the sphere and for the cylinder. For both solutions, the general refractiveindex mismatch boundary conditions, as applicable in biomedical optics, are resorted to. These solutions are validated using mesh-based Monte Carlo simulations. So validated, these solutions allow in turn to rapidly validate numerical code, based for example on finite differences or on finite elements, without requiring lengthy Monte Carlo simulations. provide reliable tool for validating numerical simulations. In the second part, iterative reconstruction for fluorescence diffuse optical tomography imaging is proposed based on an Lq-Lp framework for formulating an objective function and its regularization term. To solve the imaging inverse problem, the discretization of the light propagation model is performed using the finite difference method. The framework is used along with a multigrid mesh on a digital mouse model. The inverse problem is solved iteratively using an optimization method. For this, the gradient of the cost function with respect to the fluorescent agent’s concentration map is necessary. This is calculated using an adjoint method. Quantitative metrics resorted to in medical imaging are used to evaluate the performance of the framework under different conditions. The results obtained support this new approach based on an Lq-Lp formulation of cost functions in order to solve the inverse fluorescence problem with high quantified performance

Automated analysis and visualization of preclinical whole-body microCT data

In this thesis, several strategies are presented that aim to facilitate the analysis and visualization of whole-body in vivo data of small animals. Based on the particular challenges for image processing, when dealing with whole-body follow-up data, we addressed several aspects in this thesis. The developed methods are tailored to handle data of subjects with significantly varying posture and address the large tissue heterogeneity of entire animals. In addition, we aim to compensate for lacking tissue contrast by relying on approximation of organs based on an animal atlas. Beyond that, we provide a solution to automate the combination of multimodality, multidimensional data.* Advanced School for Computing and Imaging (ASCI), Delft, NL * Bontius Stichting inz Doelfonds Beeldverwerking, Leiden, NL * Caliper Life Sciences, Hopkinton, USA * Foundation Imago, Oegstgeest, NLUBL - phd migration 201