Fluorescence microscopy allows real-time monitoring of optical molecular probes for disease characterization, drug development, and tissue regeneration. However, when a biological sample is thicker than 1 mm, intense scattering of light would significantly degrade the spatial resolution of fluorescence microscopy. In this paper, we develop a fluorescence microtomography technique that utilizes the Monte Carlo method to image fluorescence reporters in thick biological samples. This approach is based on an l0-regularized tomography model and provides an excellent solution. Our studies on biomimetic tissue scaffolds have demonstrated that the proposed approach is capable of localizing and quantifying the distribution of optical molecular probe accurately and reliably.
We present a generalized Delta-Eddington phase function to simplify the radiative transfer equation to integral equations with respect to both photon fluence rate and flux vector. The photon fluence rate and flux can be solved from the system of integral equations. By comparing to the Monte Carlo simulation results, the solutions of the system of integral equations accurately model the photon propagation in biological tissue over a wide range of optical parameters.
Healthy tissues and tumors exhibit different optical characteristics in blood volume and oxygen sufficiency. Tumor physiology is effectively monitored by non-invasively observing the changes in oxyhemoglobin and deoxyhemoglobin concentration in tissue. In this paper, we present a practical method for quantitative assessment of hemoglobin concentration and blood oxygenation based on the diffusion theory and finite element analysis. The method incorporates prior knowledge on permissible target region, and reduced the reconstruction of chromosphere concentration to an optimization procedure with simple constrain. A numerical simulation study has been conducted by using a heterogeneous phantom. The numerical results show that the reconstruction method has been successfully applied for the reconstruction of the variation of HbO2 and HbR concentration in numerical simulation experiments.
Localization and quantification of the light sources generated by the expression of bioluminescent reporter genes is an important task in bioluminescent imaging of small animals, especially the generically engineered mice. To employ the Monte Carlo method for the light-source identification, the surfaces that define the anatomic structures of the small experimental animal is required; to perform finite element-based reconstruction computation, the volumetric mesh is a must. In this work, we proposed a Multiregional Marching Tetrahedra (MMT) method for extracting the surface and volumetric meshes from segmented CT/micro-CT (or MRI) image volume of a small experimental animal. The novel MMT method extracts triangular surface mesh and constructs tetrahedra/prisms volumetric finite element mesh for all anatomic components, including heart, liver, lung, bones etc., within one sweep over all the segmented CT slices. In comparison with the well-established Marching Tetrahedra (MT) algorithm, our MMT method takes into consideration of two more surface extraction cases within each tetrahedron, and guarantees seamless connection between anatomical components. The surface mesh is then smoothed and simplified, without losing the seamless connections. The MMT method is further enhanced to generate volumetric finite-element mesh to fill the space of each anatomical component. The mesh can then be used for finite element-based inverse computation to identify the light sources.
Noninvasive imaging of the reporter gene expression based on bioluminescence is playing an important role in the areas of cancer biology, cell biology, and gene therapy. The central problem for the bioluminescence tomography (BLT) we are developing is to reconstruct the underlying bioluminescent source distribution in a small animal using a modality fusion approach. To solve this inversion problem, a mathematical model of the mouse is built from a CT/micro-CT scan, which enables the assignment of optical parameters to various regions in the model. This optical geometrical model is used in the Monte Carlo simulation to calculate the flux distribution on the animal body surface, as a key part of the BLT process. The model development necessitates approximations in surface simplification, and so on. It leads to the model mismatches of different kinds. To overcome such discrepancies, instead of developing a mathematical model, segmented CT images are directly used in our simulation software. While the simulation code is executed, those images that are relevant are assessed according to the location of the propagating photon. Depending upon the segmentation rules including the pixel value range, appropriate optical parameters are selected for statistical sampling of the free path and weight of the photon. In this paper, we report luminescence experiments using a physical mouse phantom to evaluate this image-guided simulation procedure, which suggest both the feasibility and some advantages of this technique over the existing methods.
Optical signatures of tumor cells may be generated by expression of reporter genes encoding bioluminescent/fluorescent proteins. Bioluminescent imaging is a novel technique that identifies such light sources from the light flux detected on the surface of a small animal. This technique can effectively evaluate tumor cell growth and regression in response to various therapies in medical research, drug development and gene therapy. In this paper, the diffusion approximation is employed to describe the propagation of photons through biological tissues. Then, a practical method is proposed for localizing and quantifying bioluminescent sources from external bioluminescent signals. This method incorporates prior knowledge on permissible source regions, and transforms the inverse bioluminescent problem into a finite element-based constrained optimization procedure. This approach is validated and evaluated with ideal and noise data in numerical simulation.