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.
The most important task for bioluminescence imaging is to identify the emission source from the captured bioluminescent signal on the surface of a small tested animal. Quantitative information on the source location, geometry and intensity serves for in-vivo monitoring of infectious diseases, tumor growth, metastases in the small animal. In this paper, we present a point-spread function-based method for reconstructing the internal bioluminescent source from the surface light output flux signal. The method is evaluated for sensing the internal emission sources in nylon phantoms and within a live mouse. The surface bioluminescent signal is taken with a highly sensitive CCD camera. The results show the feasibility and efficiency of the proposed point-spread function-based method.
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.
White nylon material was chosen to make cylindrical tissue phantoms for development of bioluminescence tomography techniques. A low-level light source, delivered through an optic fiber of core diameter 200 μm, was placed at different locations on one phantom surface. The light travels through the phantom, reaches the external surface, and is captured by a liquid nitrogen-cooled CCD camera. The scattering, absorption, and anisotropy parameters of the phantom are obtained by matching the measured light transmission profiles to the profiles generated by the TracePro software. The perturbation analysis, with the homogeneous phantoms, demonstrated that the imaging system is sufficiently sensitive to capture intensity change of higher than 0.5nW/cm2 or a location shift of the light source of more than 200 microns. It is observed that the system can distinguish two point light sources with separation of about 2 mm. The perturbation analysis is also performed with the heterogeneous phantom. Based on our data, we conclude that there is inherent tomographic information in bioluminescent measures taken on the external surface of the mouse, which suggests the feasibility of bioluminescence tomography for biomedical research using the small animals, especially the mice.