Conical mirror is a preferred choice for fluorescence molecular tomography (FMT) because of its ability to collect fluorescent emission photons from the whole surface of the imaged object such as mice. Conical mirror, however, would lead to a fraction of photons to be reflected back to the mice surface, including excitation photons and emission photons, which result in inaccurate source positions and measurements errors in the FMT forward modeling and reconstruction. Based on Monte Carlo simulations, we have studied systematically the effects of multiple reflections of different conical mirror designs. We first generated a multiple reflected photon map for each design of the conical mirror, and then we applied Monte Carlo simulations to model photon propagation inside tissues. Finally, we evaluated the ratio of the multiple reflected photons to the total photons, and figured out the optimized size of the conical mirror. Our simulations demonstrated that a single conical mirror configuration could eliminate the multiple reflection issue while keep the imaging system setup simple when its small aperture radius is larger than 5 centimeters. We then fabricated a conical mirror with the optimized size according to the Monte Carlo simulation results, and performed phantom experiments with both the optimized conical mirror and the non-optimized one. Phantom experiment results show that noises in the reconstructed images are reduced with the optimized conical mirror, and the reconstruction accuracy is improved as well.
Fluorescence molecular tomography (FMT) is an important in vivo imaging modality to visualize physiological and pathological processes in small animals. However, FMT reconstruction is ill-posed and ill-conditioned due to strong optical scattering in deep tissues, which results in poor spatial resolution. It is well known that FMT image quality can be improved substantially by applying the structural guidance in the FMT reconstruction. An approach to introducing anatomical information into the FMT reconstruction is presented using the kernel method. In contrast to conventional methods that incorporate anatomical information with a Laplacian-type regularization matrix, the proposed method introduces the anatomical guidance into the projection model of FMT. The primary advantage of the proposed method is that it does not require segmentation of targets in the anatomical images. Numerical simulations and phantom experiments have been performed to demonstrate the proposed approach’s feasibility. Numerical simulation results indicate that the proposed kernel method can separate two FMT targets with an edge-to-edge distance of 1 mm and is robust to false-positive guidance and inhomogeneity in the anatomical image. For the phantom experiments with two FMT targets, the kernel method has reconstructed both targets successfully, which further validates the proposed kernel method.
Reconstruction of fluorescence molecular tomography (FMT) is an ill-posed inverse problem. Anatomical guidance in the FMT reconstruction can improve FMT reconstruction efficiently. We have developed a kernel method to introduce the anatomical guidance into FMT robustly and easily. The kernel method is from machine learning for pattern analysis and is an efficient way to represent anatomical features. For the finite element method based FMT reconstruction, we calculate a kernel function for each finite element node from an anatomical image, such as a micro-CT image. Then the fluorophore concentration at each node is represented by a kernel coefficient vector and the corresponding kernel function. In the FMT forward model, we have a new system matrix by multiplying the sensitivity matrix with the kernel matrix. Thus, the kernel coefficient vector is the unknown to be reconstructed following a standard iterative reconstruction process. We convert the FMT reconstruction problem into the kernel coefficient reconstruction problem. The desired fluorophore concentration at each node can be calculated accordingly. Numerical simulation studies have demonstrated that the proposed kernel-based algorithm can improve the spatial resolution of the reconstructed FMT images. In the proposed kernel method, the anatomical guidance can be obtained directly from the anatomical image and is included in the forward modeling. One of the advantages is that we do not need to segment the anatomical image for the targets and background.
We performed numerical simulations and phantom experiments with a conical mirror based fluorescence molecular tomography (FMT) imaging system to optimize its performance. With phantom experiments, we have compared three measurement modes in FMT: the whole surface measurement mode, the transmission mode, and the reflection mode. Our results indicated that the whole surface measurement mode performed the best. Then, we applied two different neutral density (ND) filters to improve the measurement's dynamic range. The benefits from ND filters are not as much as predicted. Finally, with numerical simulations, we have compared two laser excitation patterns: line and point. With the same excitation position number, we found that the line laser excitation had slightly better FMT reconstruction results than the point laser excitation. In the future, we will implement Monte Carlo ray tracing simulations to calculate multiple reflection photons, and create a look-up table accordingly for calibration.
Dynamic fluorescence molecular tomography (FMT) has the potential to quantify physiological or biochemical information, known as pharmacokinetic parameters, which are important for cancer detection, drug development and delivery etc. To image those parameters, there are indirect methods, which are easier to implement but tend to provide images with low signal-to-noise ratio, and direct methods, which model all the measurement noises together and are statistically more efficient. The direct reconstruction methods in dynamic FMT have attracted a lot of attention recently. However, the coupling of tomographic image reconstruction and nonlinearity of kinetic parameter estimation due to the compartment modeling has imposed a huge computational burden to the direct reconstruction of the kinetic parameters. In this paper, we propose to take advantage of both the direct and indirect reconstruction ideas through a variable splitting strategy under the augmented Lagrangian framework. Each iteration of the direct reconstruction is split into two steps: the dynamic FMT image reconstruction and the node-wise nonlinear least squares fitting of the pharmacokinetic parameter images. Through numerical simulation studies, we have found that the proposed algorithm can achieve good reconstruction results within a small amount of time. This will be the first step for a combined dynamic PET and FMT imaging in the future.
We have developed a new fluorescence molecular tomography (FMT) imaging system, in which we utilized a phase shifting method to extract the mouse surface geometry optically and a rotary laser scanning approach to excite fluorescence molecules and acquire fluorescent measurements on the whole mouse body. Nine fringe patterns with a phase shifting of 2π/9 are projected onto the mouse surface by a projector. The fringe patterns are captured using a webcam to calculate a phase map that is converted to the geometry of the mouse surface with our algorithms. We used a DigiWarp approach to warp a finite element mesh of a standard digital mouse to the measured mouse surface thus the tedious and time-consuming procedure from a point cloud to mesh is avoided. Experimental results indicated that the proposed method is accurate with errors less than 0.5 mm. In the FMT imaging system, the mouse is placed inside a conical mirror and scanned with a line pattern laser that is mounted on a rotation stage. After being reflected by the conical mirror, the emitted fluorescence photons travel through central hole of the rotation stage and the band pass filters in a motorized filter wheel, and are collected by a CCD camera. Phantom experimental results of the proposed new FMT imaging system can reconstruct the target accurately.
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