High-Performance Fluorescence Molecular Tomography through Shape-Based Reconstruction Using Spherical Harmonics Parameterization

BackgroundImplicit shape-based reconstruction method in fluorescence molecular tomography (FMT) is capable of achieving higher image clarity than image-based reconstruction method. However, the implicit shape method suffers from a low convergence speed and performs unstably due to the utilization of gradient-based optimization methods. Moreover, the implicit shape method requires priori information about the number of targets.MethodsA shape-based reconstruction scheme of FMT with a cosinoidal level set method is proposed in this paper. The Heaviside function in the classical implicit shape method is replaced with a cosine function, and then the reconstruction can be accomplished with the Levenberg–Marquardt method rather than gradient-based methods. As a result, the priori information about the number of targets is not required anymore and the choice of step length is avoided.ResultsNumerical simulations and phantom experiments were carried out to validate the proposed method. Results of the proposed method show higher contrast to noise ratios and Pearson correlations than the implicit shape method and image-based reconstruction method. Moreover, the number of iterations required in the proposed method is much less than the implicit shape method.ConclusionsThe proposed method performs more stably, provides a faster convergence speed than the implicit shape method, and achieves higher image clarity than the image-based reconstruction method.

BackgroundThe inverse problem of fluorescent molecular tomography (FMT) often involves complex large-scale matrix operations, which may lead to unacceptable computational errors and complexity. In this research, a tree structured Schur complement decomposition strategy is proposed to accelerate the reconstruction process and reduce the computational complexity. Additionally, an adaptive regularization scheme is developed to improve the ill-posedness of the inverse problem.MethodsThe global system is decomposed level by level with the Schur complement system along two paths in the tree structure. The resultant subsystems are solved in combination with the biconjugate gradient method. The mesh for the inverse problem is generated incorporating the prior information. During the reconstruction, the regularization parameters are adaptive not only to the spatial variations but also to the variations of the objective function to tackle the ill-posed nature of the inverse problem.ResultsSimulation results demonstrate that the strategy of the tree structured Schur complement decomposition obviously outperforms the previous methods, such as the conventional Conjugate-Gradient (CG) and the Schur CG methods, in both reconstruction accuracy and speed. As compared with the Tikhonov regularization method, the adaptive regularization scheme can significantly improve ill-posedness of the inverse problem.ConclusionsThe methods proposed in this paper can significantly improve the reconstructed image quality of FMT and accelerate the reconstruction process.

Fluorescence molecular tomography (FMT) is an important tool for life science, which can noninvasive real-time three-dimensional (3-D) visualization for fluorescence source location. FMT is widely used in tumor research due to its high-sensitive and low cost. However, the reconstruction of FMT is difficult. Although the reconstruction methods of FMT have developed rapidly in recent years, the morphological reconstruction of FMT is still a challenge problem. Thus, the purpose of this study is to realize the morphological reconstruction performance of FMT in glioma research. In this study, group sparsity was used as a new priori information for FMT. Besides sparsity, group sparsity also takes the group structure of the fluorescent sources, which can maintain the morphological information of the sources. Fused LASSO method (FLM) was proved it can efficiently model the group sparsity prior. Thus, we utilize FLM to reconstruct the morphological information of glioma. Furthermore, to reduce the influence of the high scattering of skull, we modified the FLM for improving the accuracy of morphological reconstruction. Glioma numerical simulation model and in vivo glioma model were established to evaluate the performance of morphological reconstruction of the proposed method. The results demonstrated that the proposed method was efficient to reconstruct the morphological information of glioma. Group sparsity priori can effectively improve the morphological accuracy of FMT reconstruction. Group sparsity can maintain the morphological information of fluorescent sources effectively, which has great application potential in FMT. The group sparsity based methods can realize the morphological reconstruction, which is of great practical significance in tumor research.

Fluorescence imaging is a cornerstone of preclinical research, yet its utility is hindered by semi‐quantitative limitations due to light scattering. Fluorescence molecular tomography (FMT) aims to overcome these constraints by generating 3D fluorophore maps through model‐based reconstruction. However, background fluorescence, resulting from non‐specific probe binding and autofluorescence, significantly compromises FMT image quality with severe artifacts. Solution decomposition FMT (SD‐FMT) is introduced, an innovative reconstruction method that employs an advanced prior model to decompose the solution into multiple stochastic components. The reformulated inverse problem is resolved using a hybrid projection method (HPM), ensuring robustness and efficiency. Validated through exhaustive phantom and in vivo proof‐of‐concept studies, SD‐FMT represents a paradigm shift in FMT reconstruction, with significant implications for expanding applications in preclinical research and advancing fluorescence‐guided surgical navigation.

In vivo fluorescence molecular tomography (FMT) has been a popular functional imaging modality in research labs in the past two decades. One of the major difficulties of FMT lies in the ill-posed and ill-conditioned nature of the inverse problem in reconstructing the distribution of fluorophores inside objects. The popular regularization methods based on L2, L1 and total variation (TV ) norms have been applied in FMT reconstructions. The non-convex Lq(0 < q < 1) semi-norm and Log function have also been studied recently. In this paper, we adopt a uniform optimization transfer framework for these regularization methods in FMT and compare their individual, as well as the combined effects on both small, localized targets, such as tumors in the early stage, and large targets, such as liver. Numerical simulation studies and phantom experiments have been carried out, and we found that Lq with q near 1/2 performs the best in reconstructing small targets, while joint L2 and Log performs the best for large targets.

Fluorescence molecular tomography (FMT) plays an important role in in vivo small animal imaging. However, due to the diffusive nature of photon propagation in biological tissues, FMT suffers from a low spatial resolution, which limits its capability of resolving the distribution of fluorescent biomarkers. In this paper, we investigate the effect of functional and structural a priori information on the accuracy of FMT reconstruction by a hybrid FMT and X-ray computed tomography imaging system. The results from numerical simulation and phantom experiments suggest that the fluorescence targets embedded in heterogeneous medium can be localized when structural a priori information is utilized to constrain the reconstruction process. In addition, both the functional and structural a priori information are essential for the recovery of fluorophore concentration.

Fluorescence Molecular Tomography (FMT) is one of the most important preclinical research techniques, which can obtain three-dimensional reconstruction of tumors in mouse in vivo. However, the ill-posedness of FMT makes its reconstruction a challenging problem. Therefore, more effective, robust, and accurate reconstruction methods are needed to be developed to solve the FMT reconstruction problem. In this paper, a reconstruction method named multipath subspace pursuit (MSP) is applied to solve the FMT problem. At the end of an iteration, the MSP method creates several candidate support set. Through evaluating the normal of final residual vector, the best candidate can be selected as the final support set. Then the support set is used for reconstructing sense matrix to achieve the goal of FMT reconstruction. In order to verity the reconstruction result of the proposed MSP method, the simulated experiment of triple fluorescent sources and quantitative analyses of position error and relative intensity error for the experiment have been conducted. The MSP method obtains satisfactory results, and the source position error is below 1 mm. Moreover, the computation time of the MSP method is about one order of magnitude less than iterated shrinkage with the L1-norm (ISlL1) method. The MSP method not only can obtain the result of robustness but also can reduce the artifacts in the background. The above results revealed the MSP method for the potential FMT application.

Fluorescence Molecular Tomography is an optical imaging technique which aims at reconstructing the 3D distribution of fluorescent markers in bio-tissues based on surface measurements of emitted photons and a model of light propagation. The gold standard of accuracy in creating this light propagation model is the Monte Carlo method (MC), which simulates the path of photon packets through a discretized model of the tissue. One drawback of MC is the computational burden associated with its stochastic nature. Mesh based MC are computational implementations of MC techniques with favorable computational costs. Herein, we investigate the effects of locally refining a mesh discretization on reconstruction accuracy in mesh based Fluorescence Molecular Tomography. Using a mouse model created from μCT data and average murine optical properties, we are investigating the performances of mesh refinement strategies in reconstructing an 48.9 mm 3 fluorescence inclusion in the center of the model. Iterative mesh optimization is applied to the inverse problem in which after each reconstruction, the mesh is refined in the area of interest. Performance of the method is evaluated in terms of in volume and center of mass position of the inclusion compared to the ground truth. Our preliminary results indicate that accuracy improves with each refinement until convergence. Moreover, a method for rescaling analytically the forward model to fit each new mesh is also proposed in order to reduce the computational expense of the procedure while maintaining the improvements in accuracy

Fluorescence molecular tomography (FMT) is a promising imaging technique to noninvasively resolve the three-dimensional spatial distributions of fluorescent targets associated with molecular and cellular functions. The reconstruction quality, including the localization and quantitative accuracy, are greatly improved by the use of normalized Born ratio method. However, the reconstruction quality is still far from reaching their potential of accurately visualizing biological information with a high localization and quantitative accuracy. It is because the inverse problem of FMT is severely ill-posed and is easily affected by the noise in the measurement data. Here we provided a scaling normalization method for FMT to restrain the influence of excitation noise on fluorescence molecular tomography reconstruction. In this method, we use the total intensity of the excitation data detected by CCD camera to normalize the fluorescence measurements. It's feasible to suppress excitation noise when compared with the normalized Born ration method. Furthermore, by applying singular-value decomposition to the forward matrices, we find that the method we proposed has more useful singular values than the normalized Born ratio method which means that it reduces the ill-posedness of FMT inverse problem. To examine its performance, we perform phantom experiments with a hybrid FMT-XCT system. To check the robustness of our method to excitation noise, we add 10% Gaussian white noise to the measured excitation data. The reconstruction results are compared with those of the normalized Born ratio method. The results indicate that our method has a better reconstruction results and a better noise robustness.

Accurate small animal surface reconstruction is important for full angle non-contact fluorescence molecular tomography (FMT) systems. In this letter, an optimal surface reconstruction method for FMT is proposed. The proposed method uses a line search method to minimize the mismatch between the reconstructed three-dimensional (3D) surface and the projected object silhouette at different angles. The results show that the mean mismatches of the 3D surfaces generated on three live anesthetized mice are all less than two charge coupled device (CCD) pixels (0.154 mm). With the accurately reconstructed 3D surface, in-vivo FMT is also performed.

We present a new framework for 3D surface object classification that combines a powerful shape description method with suitable pattern classification techniques. Spherical harmonic parameterization and normalization techniques are used to describe a surface shape and derive a dual high dimensional landmark representation. A point distribution model is applied to reduce the dimensionality. Fisher's linear discriminants and support vector machines are used for classification. Several feature selection schemes are proposed for learning better classifiers. After showing the effectiveness of this framework using simulated shape data, we apply it to real hippocampal data in schizophrenia and perform extensive experimental studies by examining different combinations of techniques. We achieve best leave-one-out cross-validation accuracies of 93% (whole set, N = 56) and 90% (right-handed males, N = 39), respectively, which are competitive with the best results in previous studies using different techniques on similar types of data. Furthermore, to help medical diagnosis in practice, we employ a threshold-free receiver operating characteristic (ROC) approach as an alternative evaluation of classification results as well as propose a new method for visualizing discriminative patterns.

Conventional fluorescence molecular tomography (FMT) reconstruction requires photons penetrating the whole object, which limits its applications to small animals. However, by utilizing reflective photons, fluorescence distribution near the surface could be reconstructed regardless of the object size, which may extend the applications of FMT to surgical navigation and so on. Therefore, time-domain reflective fluorescence molecular tomography (TD-rFMT) is proposed in this paper. The system excites and detects the emission light from the same angle within a field of view of 5 cm. Because the detected intensities of targets depend strongly on the depth, the reconstruction of targets in deep regions would be evidently affected. Therefore, a fluorescence yield reconstruction method with depth regularization and a weighted separation reconstruction strategy for lifetime are developed to enhance the performance for deep targets. Through simulations and phantom experiments, TD-rFMT is proved capable of reconstructing fluorescence distribution within a 2.5-cm depth with accurate reconstructed yield, lifetime, and target position(s).

In conventional fluorescence molecular tomography, the distribution of fluorescent contrast agents is reconstructed with the assumption of constant concentration during data acquisition for each image frame. However, the concentration of fluorescent contrast target is usually time-varying in experiments or in-vivo studies. In this case, the reconstruction methods cannot be directly applied to the fluorescence measurements without considering the time-varying effects of concentration. We propose a modified forward model by dividing the fluorescence yield distribution into two parts: one is a constant representing the spatial distribution of the fluorescent target and the other is an impact factor representing the effects of the concentration change and other possible factors. By extracting spatial distribution information from the reconstruction result, the location and volume of the fluorescent target can be obtained accurately. Both simulation and phantom experiments are carried out and the results indicate that, by using the modified forward model, the quality of reconstruction could be significantly improved in terms of accurate localization and strong anti-noise ability.

The present full-angle, free-space fluorescence molecular tomography (FMT) system uses a step-by-step strategy to acquire measurements, which consumes time for both the rotation of the object and the integration of the charge-coupled device (CCD) camera. Completing the integration during the rotation is a more time-efficient strategy called synchronous data acquisition. However, the positions of sources and detectors in this strategy are not stationary, which is not taken into account in the conventional reconstruction algorithm. In this paper we propose a reconstruction algorithm based on the finite element method (FEM) to overcome this problem. Phantom experiments were carried out to validate the performance of the algorithm. The results show that, compared with the conventional reconstruction algorithm used in the step-by-step data acquisition strategy, the proposed algorithm can reconstruct images with more accurate location data and lower relative errors when used with the synchronous data acquisition strategy.

We propose a multimodal imaging method by combining continuous-wave fluorescence molecular tomography (FMT) with time-domain near infrared optical tomography (NIROT). Phantom experiments have demonstrated that using the NIROT-derived optical properties improves the FMT reconstruction significantly.