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.
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) 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.
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.
Bioluminescence tomography (BLT) is a promising optical molecular imaging technique in preclinical research. One key problem for BLT is how to deal with the severe ill-posedness and obtain accurate and stable reconstruction. We propose a penalty method for recovering bioluminescence sources, in which we transform reconstruction of BLT into an L1/2-norm penalty problem and solve it via a nonmonotone proximal gradient method with a suitable penalty parameter update scheme. Simulations and phantom experiments based on multispectral measurements were designed to evaluate the proposed reconstruction method. The encouraging results show that the proposed method has better reconstruction accuracy and image quality than the comparative methods.
Sparse regularization methods have been widely used in fluorescence molecular tomography (FMT) for stable three-dimensional reconstruction. Generally, ℓ1-regularization-based methods allow for utilizing the sparsity nature of the target distribution. However, in addition to sparsity, the spatial structure information should be exploited as well. A joint ℓ1 and Laplacian manifold regularization model is proposed to improve the reconstruction performance, and two algorithms (with and without Barzilai–Borwein strategy) are presented to solve the regularization model. Numerical studies and in vivo experiment demonstrate that the proposed Gradient projection-resolved Laplacian manifold regularization method for the joint model performed better than the comparative algorithm for ℓ1 minimization method in both spatial aggregation and location accuracy.
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.
Fluorescence molecular tomography (FMT) is a non-invasive technique that allows three-dimensional visualization of fluorophore in vivo in small animals. In practical applications of FMT, however, there are challenges in the image reconstruction since it is a highly ill-posed problem due to the diffusive behaviour of light transportation in tissue and the limited measurement data. In this paper, we presented an iterative algorithm based on an optimization problem for three dimensional reconstruction of fluorescent target. This method alternates weighted algebraic reconstruction technique (WART) with steepest descent method (SDM) for image reconstruction. Numerical simulations experiments and physical phantom experiment are performed to validate our method. Furthermore, compared to conjugate gradient method, the proposed method provides a better three-dimensional (3D) localization of fluorescent target.
KEYWORDS: Principal component analysis, Optical properties, Luminescence, Lung, Heart, Tomography, In vivo imaging, Tissues, 3D modeling, 3D image processing
Challenges remain in resolving drug (fluorescent biomarkers) distributions within small animals by fluorescence diffuse optical tomography (FDOT). Principal component analysis (PCA) provides the capability of detecting organs (functional structures) from dynamic FDOT images. However, the resolving performance of PCA may be affected by various experimental factors, e.g., the noise levels in measurement data, the variance in optical properties, the number of acquired frames, and so on. To address the problem, based on a simulation model, we analyze and compare the performance of PCA when applied to three typical sets of experimental conditions (frames number, noise level, and optical properties). The results show that the noise is a critical factor affecting the performance of PCA. When input data containing a low noise (<5%), by a short (e.g., 6 frame) projection sequence, we can resolve the poly(DL-lactic-coglycolic acid)/indocynaine green (PLGA/ICG) distributions in heart and lungs, even though there are great variances in optical properties. In contrast, when 20% Gaussian noise is added to the input data, it hardly resolves the distributions of PLGA/ICG in heart and lungs even though accurate optical properties are used. However, with an increased number of frames, the resolving performance of PCA may gradually recover.
As an important optical molecular imaging technique, bioluminescence tomography (BLT) offers an inexpensive and
sensitive means for non-invasively imaging a variety of physiological and pathological activities at cellular and
molecular levels in living small animals. The key problem of BLT is to recover the distribution of the internal
bioluminescence sources from limited measurements on the surface. Considering the sparsity of the light source
distribution, we directly formulate the inverse problem of BLT into an l0-norm minimization model and present a
smoothed l0-norm (SL0) based reconstruction algorithm. By approximating the discontinuous l0 norm with a suitable
continuous function, the SL0 norm method solves the problem of intractable computational load of the minimal l0 search as well as high sensitivity of l0-norm to noise. Numerical experiments on a mouse atlas demonstrate that the proposed
SL0 norm based reconstruction method can obtain whole domain reconstruction without any a priori knowledge of the
source permissible region, yielding almost the same reconstruction results to those of l1 norm methods.
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.
As a promising tool for in-vivo molecular imaging of small animals, Bioluminescence Tomography (BLT) aims at the quantitative reconstruction of the bioluminescent source distribution from the detected optical signals on the body surface. Mathematically, BLT is a highly ill-posed inverse problem per se. Most existing works are based on Tikhonov regularization in which the selection of the proper regular parameter is quite difficult. In this paper, two direct
regularization methods, truncated singular value decomposition (TSVD) and truncated total least squares (TTLS), as well
as two iterative regularization methods, conjugate gradient least squares (CGLS) and least squares QR decomposition
(LSQR), are applied to the inverse problem in BLT, with the finite element method solving the diffusion equation. In the
numerical simulation, a heterogeneous phantom is designed to compare and evaluate the four methods. The results show
that all the four methods can reconstruct the position of bioluminescence sources accurately and are more convenient in
the determination of regularization parameter than Tikhonov method. In addition, with a priori knowledge of the source
permissible region employed in the reconstruction, the iterative methods are faster than the two direct methods. Among
the four methods, LSQR performs quite stably when both model noise and measure noise are considered.
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