Xibo Ma

Sparsity-Promoting Tomographic Fluorescence Imaging With Simplified Spherical Harmonics Approximation

Fluorescence molecular tomography has become a promising technique for in vivo small animal imaging and has many potential applications. Due to the ill-posed and the ill-conditioned nature of the problem, Tikhonov regularization is generally adopted to stabilize the solution. However, the result is usually over-smoothed. In this letter, the third-order simplified spherical harmonics approximation to radiative transfer equation is utilized to model the photon propagation within biological tissues. Considering the sparsity of the fluorescent sources, we replace Tikhonov method with an iteratively reweighted scheme. By dynamically updating the weight matrix, L1-norm regularization can be approximated, which can promote the sparsity of the solution. Simulation study shows that this method can preserve the sparsity of the fluorescent sources within heterogeneous medium, even with very limited measurement data.

Efficient reconstruction method for L1 regularization in fluorescence molecular tomography.

Fluorescence molecular tomography (FMT) is a promising technique for in vivo small animal imaging. In this paper, the sparsity of the fluorescent sources is considered as the a priori information and is promoted by incorporating L1 regularization. Then a reconstruction algorithm based on stagewise orthogonal matching pursuit is proposed, which treats the FMT problem as the basis pursuit problem. To evaluate this method, we compare it to the iterated-shrinkage-based algorithm with L1 regularization. Numerical simulations and physical experiments show that the proposed method can obtain comparable or even slightly better results. More importantly, the proposed method was at least 2 orders of magnitude faster in these experiments, which makes it a practical reconstruction algorithm.

SM5-1-Conjugated PLA nanoparticles loaded with 5-fluorouracil for targeted hepatocellular carcinoma imaging and therapy

SM5-1 is a humanized mouse antibody which has a high binding specificity for a membrane protein of about 230 kDa overexpressed in hepatocellular carcinoma (HCC), melanoma and breast cancer. In this study, SM5-1-conjugated poly D, L (lactide-coglycolide) (PLA) PLA containing Cy7 (PLA-Cy7-SM5-1) was prepared to study the targeting specificity of the bioconjugate to HCC-LM3-fLuc cell. Then, SM5-1-conjugated PLA containing 5-fluorouracil (5-FU) (PLA-5FU-SM5-1) and PLA containing 5-FU (PLA-5FU) were prepared for treatment of subcutaneous HCC-LM3-fLuc tumor mice. The results showed that PLA-5FU-SM5-1, PLA-5FU and 5-FU induced a 45.07%, 23.56% and 19.05% tumor growth inhibition rate, respectively, on day 31 post-treatment as determined by bioluminescent intensity. In addition, in order to evaluate the antitumor efficacy of PLA-5FU-SM5-1, HCC-LM3-fLuc cells were injected into the liver to establish the experimental orthotopic liver tumor models. The experiments showed that PLA-5FU-SM5-1, PLA-5FU and 5-FU induced a 53.24%, 31.00%, and 18.11% tumor growth inhibition rate, respectively, on day 31 post-treatment determined by the bioluminescent intensity of the abdomen in tumor-bearing mice. Furthermore, we have calculated the three-dimensional location of the liver cancer in mice using a multilevel adaptive finite element algorithm based on bioluminescent intensity decay calibration. The reconstruction results demonstrated that PLA-5FU-SM5-1 inhibited the tumor rapid progression, which were consistent with the results of subcutaneous tumor mice experiments and in vitro cell experiment results.

A trust region method in adaptive finite element framework for bioluminescence tomography

Bioluminescence tomography (BLT) is an effective molecular imaging (MI) modality. Because of the ill-posedness, the inverse problem of BLT is still open. We present a trust region method (TRM) for BLT source reconstruction. The TRM is applied in the source reconstruction procedure of BLT for the first time. The results of both numerical simulations and the experiments of cube phantom and nude mouse draw us to the conclusion that based on the adaptive finite element (AFE) framework, the TRM works in the source reconstruction procedure of BLT. To make our conclusion more reliable, we also compare the performance of the TRM and that of the famous Tikhonov regularization method after only one step of mesh refinement of the AFE framework. The conclusion is that the TRM can get faster and better results after only one mesh refinement step of AFE framework than the Tikhonov regularization method when handling large scale data. In the TRM, all the parameters are fixed, while in the Tikhonov method the regularization parameter needs to be well selected.

Dual-modality monitoring of tumor response to cyclophosphamide therapy in mice with bioluminescence imaging and small-animal positron emission tomography.

The purpose of this study was to noninvasively monitor the therapeutic efficacy of cyclophosphamide (CTX) in a mouse model by dual-modality molecular imaging: positron emission tomography (PET) and bioluminescence imaging (BLI). Firefly luciferase (fLuc) transfected HCC-LM3-fLuc human hepatocellular carcinoma cells were injected subcutaneously into BALB/c nude mice to establish the experimental tumor model. Two groups of HCC-LM3-fLuc tumor-bearing mice (n = 7 per group) were treated with saline or CTX (100 mg/kg on days 0, 2, 5, and 7). BLI and 18F-fluorodeoxyglucose (18F-FDG) PET scans were done to evaluate the treatment efficacy. CTX induced a 25.25 ± 13.13% and 35.91 ± 25.85% tumor growth inhibition rate on days 9 and 12 posttreatment, respectively, as determined by BLI. A good linear correlation was found between the tumor sizes measured by caliper and the BLI signals determined by optical imaging (R 2 = .9216). 18F-FDG imaging revealed a significant uptake reduction in the tumors of the CTX-treated group compared to that in the saline control group (5.30 ± 1.97 vs 3.00 ± 2.11% ID/g) on day 16 after CTX treatment. Dual-modality molecular imaging using BLI and small-animal PET can play important roles in the process of chemotherapy and will provide noninvasive and reliable monitoring of the therapeutic response.

Comparison of permissible source region and multispectral data using efficient bioluminescence tomography method

As a novel molecular imaging technology, bioluminescence tomography (BLT) has become an important tool for biomedical research in recent years, which can perform a quantitative reconstruction of an internal light source distribution with the scattered and transmitted bioluminescent signals measured on the external surface of a small animal. However, BLT is severely ill-posed because of complex photon propagation in the biological tissue and limited boundary measured data with noise. Therefore, sufficient a priori knowledge should be fused for the uniqueness and stability of BLT solution. Permissible source region strategy and spectrally resolved measurements are two kinds of a priori knowledge commonly used in BLT reconstruction. This paper compares their performance with simulation and in vivo heterogeneous mouse experiments. In order to improve the efficiency of large-scale source restoration, this paper introduces an efficient iterative shrinkage thresholding method that not only has faster convergence rate but also has better reconstruction accuracy than the modified Newton-type optimization approach. Finally, a discussion of these two kinds of a priori knowledge is given based on the comparison results.

The CUBLAS and CULA based GPU acceleration of adaptive finite element framework for bioluminescence tomography

In molecular imaging (MI), especially the optical molecular imaging, bioluminescence tomography (BLT) emerges as an effective imaging modality for small animal imaging. The finite element methods (FEMs), especially the adaptive finite element (AFE) framework, play an important role in BLT. The processing speed of the FEMs and the AFE framework still needs to be improved, although the multi-thread CPU technology and the multi CPU technology have already been applied. In this paper, we for the first time introduce a new kind of acceleration technology to accelerate the AFE framework for BLT, using the graphics processing unit (GPU). Besides the processing speed, the GPU technology can get a balance between the cost and performance. The CUBLAS and CULA are two main important and powerful libraries for programming on NVIDIA GPUs. With the help of CUBLAS and CULA, it is easy to code on NVIDIA GPU and there is no need to worry about the details about the hardware environment of a specific GPU. The numerical experiments are designed to show the necessity, effect and application of the proposed CUBLAS and CULA based GPU acceleration. From the results of the experiments, we can reach the conclusion that the proposed CUBLAS and CULA based GPU acceleration method can improve the processing speed of the AFE framework very much while getting a balance between cost and performance.

Early detection of liver cancer based on bioluminescence tomography

As a new modality of molecular imaging, bioluminescence imaging has been widely used in tumor detection and drug evaluation. However, BLI cannot present the depth of information for internal diseases such as a liver tumor in situ or a lung tumor in situ. In this paper, we describe a bioluminescence tomography (BLT) method based on the bioluminescent intensity attenuation calibration and applied it to the early detection of liver cancer in situ. In comparison with BLT without calibration, this method could improve the reconstruction accuracy by more than 10%. In comparison with micro-computed tomography and other traditional imaging modalities, this method can detect a liver tumor at a very early stage and provide reliable location information.

Compressed sensing photoacoustic imaging based on fast alternating direction algorithm

Photoacoustic imaging (PAI) has been employed to reconstruct endogenous optical contrast present in tissues. At the cost of longer calculations, a compressive sensing reconstruction scheme can achieve artifact-free imaging with fewer measurements. In this paper, an effective acceleration framework using the alternating direction method (ADM) was proposed for recovering images from limited-view and noisy observations. Results of the simulation demonstrated that the proposed algorithm could perform favorably in comparison to two recently introduced algorithms in computational efficiency and data fidelity. In particular, it ran considerably faster than these two methods. PAI with ADM can improve convergence speed with fewer ultrasonic transducers, enabling a high-performance and cost-effective PAI system for biomedical applications.

Adaptive regularized method based on homotopy for sparse fluorescence tomography

Determining an appropriate regularization parameter is often challenging work because it has a narrow range and varies with problems, which is likely to lead to large reconstruction errors. In this contribution, an adaptive regularized method based on homotopy is presented for sparse fluorescence tomography reconstruction. Due to the adaptive regularization strategy, the proposed method is always able to reconstruct sources accurately independent of the estimation of the regularization parameter. Moreover, the proposed method is about two orders of magnitude faster than the two contrasting methods. Numerical and in vivo mouse experiments have been employed to validate the robustness and efficiency of the proposed method.