SignificanceFluorescence molecular tomography (FMT) is a promising imaging modality, which has played a key role in disease progression and treatment response. However, the quality of FMT reconstruction is limited by the strong scattering and inadequate surface measurements, which makes it a highly ill-posed problem. Improving the quality of FMT reconstruction is crucial to meet the actual clinical application requirements.AimWe propose an algorithm, neighbor-based adaptive sparsity orthogonal least square (NASOLS), to improve the quality of FMT reconstruction.ApproachThe proposed NASOLS does not require sparsity prior information and is designed to efficiently establish a support set using a neighbor expansion strategy based on the orthogonal least squares algorithm. The performance of the algorithm was tested through numerical simulations, physical phantom experiments, and small animal experiments.ResultsThe results of the experiments demonstrated that the NASOLS significantly improves the reconstruction of images according to indicators, especially for double-target reconstruction.ConclusionNASOLS can recover the fluorescence target with a good location error according to simulation experiments, phantom experiments and small mice experiments. This method is suitable for sparsity target reconstruction, and it would be applied to early detection of tumors.
Fluorescence molecular tomography (FMT) is an important molecular imaging technique for tumor detection in early stage. In realistic research, due to the spread and metastasis of tumor cells, it is necessary to comprehensively analyze multiple tumor regions for cancer staging studies. Therefore, high-precision multi-light source reconstruction results are required for quantitative analysis in FMT research. However, the existing methods perform well in the reconstruction of single fluorescent source but may fail in reconstructing multiple targets, which is an obstacle for FMT practical application. In this paper, we proposed a multi-target reconstruction strategy for Fluorescence Molecular Tomography based on Blind Source Separation (BSS) by converting multi-target reconstructions into multiple single-target reconstructions. It is a breakthrough work in multi-target reconstruction for cancer staging using optical molecular technique. Numerical simulation experiments proved that it had the ability of multi-source resolution for FMT in accurate location and morphology recovery. The encouraging results demonstrate significant effectiveness and potential of our method for preclinical FMT applications.
Bioluminescence tomography (BLT) is an effective noninvasive molecular imaging modality, it has shown great potential for studying and monitoring disease progression in pre-clinical imaging. As the BLT is an inherent highly ill-posed inverse problem, it is still a challenge to obtain an accurate reconstruction result. Some algorithms have been proposed to solve highly ill posedness of inverse problems. Nevertheless, Existing methods always need to consume large time or have low interpretability. Thus, in this paper, we proposed a novel model-driven deep learning network, which unfolding the Fast Iterative Shrinkage Thresholding Algorithm (FISTA) algorithm into a deep network, named FISTA-Net to overcome the above shortcoming. FISTA-Net is formed from three modules, gradient descent module, proximal mapping module and accelerate module. Key parameters of FISTA-Net including the gradient step size, thresholding value are learned from training data. The experimental results, evaluated both visually and quantitatively, show that the FISTA-Net can achieve a high-quality reconstruction result of BLT.
Bioluminescence tomography (BLT) reconstruction is an ill-posed problem. A class of strategy based on the permissible region (PR) reduces the ill-posed by reducing the space. However, in multi-objective reconstruction, the strategy is challenging to fit the sources of different positions. In this study, a subspace decision (SD) method is proposed, which transforms the traditional single permissible region into multiple spatially continuous subspaces by clustering, and performs spatial shrinkage optimization for each of them. In addition, a plug-and-play sliding single polyline module is introduced to analyze and cluster the reconstruction results each time to obtain the number and distribution of subspaces contained in the results. SD method does not rely on any specific reconstruction or clustering algorithm, so it has great flexibility. Experiment results show that the SD approach can more accurately obtain the spatial distribution information of different numbers of sources distributed in different locations and ensure the quality of multi-source BLT reconstruction. Keywords: Bioluminescence Tomography, Inverse Problem, Subspace, Clustering, Permissible Region.
Bioluminescence tomography (BLT) is a promising molecular imaging tool in monitoring non-invasively physiological and pathological processes in vivo at the cellular and molecular levels. And the radiative transfer equation (RTE) has successfully been used as a standard model for describing the propagation of visible and near infrared photons trough biological tissues. However in practical application, implementation of RTE is extremely complicated for complex biological tissue. And several approximations of the RTE were applied to model the light transport in a turbid medium, such as the diffusion equation (DE) and the simplified spherical harmonic approximation equation (SPN). However, DE provides a high computational efficiency and is only valid in the high scattering region, while SPN has a large demand for memory space, which makes it difficult for SPN to be used on the fine mesh and limits its application in practice. In this paper, we provided a new finite element mesh regrouping-based hybrid light transport model in BLT. Based on the optical property of biological tissue, the finite element mesh were grouped into high-scattering and low-scattering regions. And based on the theory of light transport, hybrid third-order simplified spherical harmonic approximate–diffusion equation model (HSDM) was used to forward light transport model. In numerical simulation experiments, accuracy and efficiency of our proposed method were evaluated. Results showed that the hybrid light transport model achieved a better balance between accuracy and efficiency compared with the DE and the SP3 models. And it was best suited as a light transport model for Bioluminescence Tomography.
Cerenkov luminescence tomography (CLT), as a promising optical molecular imaging modality, can be applied
to cancer diagnostic and therapeutic. Most researches about CLT reconstruction are based on the finite element
method (FEM) framework. However, the quality of FEM mesh grid is still a vital factor to restrict the accuracy
of the CLT reconstruction result. In this paper, we proposed a multi-grid finite element method framework,
which was able to improve the accuracy of reconstruction. Meanwhile, the multilevel scheme adaptive algebraic
reconstruction technique (MLS-AART) based on a modified iterative algorithm was applied to improve the
reconstruction accuracy. In numerical simulation experiments, the feasibility of our proposed method were
evaluated. Results showed that the multi-grid strategy could obtain 3D spatial information of Cerenkov source
more accurately compared with the traditional single-grid FEM.
The diffusion approximation of the radiative transport equation is the most widely used model in current researches on fluorescence molecular tomography (FMT), which is limited in some low or zero scattering regions. Recently, the simplified spherical harmonics equations (SPN) model has attracted much attention in modeling the light propagation in small tissue geometries at visible and near-infrared wavelengths. In this paper, we report an efficient numerical method for FMT that combines the advantage of SPN model and hp-FEM. For comparison purposes, hp-FEM and h-FEM are respectively applied in the reconstruction process with diffusion model and SPN model. Simulation experiments on a 3D digital mouse atlas are designed to evaluate the reconstruction methods in terms of the location and the reconstructed fluorescent yield. The experimental results demonstrate that hp-FEM with SPN model, yield more accurate results than h-FEM with DA model does. And the reconstructed results show the potential and feasibility of the proposed approach.
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