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.
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) 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.
Bioluminescence tomography (BLT) can reconstruct internal bioluminescent source from the surface measurements. However, multiple sources resolving of BLT is always a challenge. In this work, a comparative study on hybrid clustering algorithm, synchronization-based clustering algorithm and iterative self-organizing data analysis technique algorithm for multiple sources recognition of BLT is conducted. Simulation experiments on two and three sources reconstruction are demonstrated the performances of these three algorithms. The results show that the iterative selforganizing data analysis technique is more suitable for the closer multiple-targets and the other two algorithms are suitable for distant targets. Moreover, iterative self-organizing data analysis technique has the least computing time.
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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