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Purpose: Magnetoacoustic tomography with magnetic induction (MAT-MI) is a technique that utilizes the acoustic signals induced by magnetic stimulation to reconstruct the electrical impedance distribution in biological tissues. Most algorithms ignored the fact that acoustic properties in human tissues are heterogeneous, which lead to distortion and blurring of small reconstructed objects. In this study, a novel algorithm is proposed for exact reconstruction of the sound source distribution in acoustic heterogeneous tissues., Methods: Based on the ring transducer array, we develop an algorithm which combines algebraic reconstruction technique (ART) and time reversal method. Different to existing reconstruction methods, the ultrasonic transmission tomography (UTT) and the MAT-MI can be completed in same system, which decreases the system complexity. The sound velocity distribution is reconstructed with the ART so that the propagation time of the magnetoacoustic signals in the heterogeneous tissue is corrected. And then, the sound source image is reconstructed based on the time reversal method from new sound pressure data. Both numerical simulations and phantom experiments are established to validate the proposed method., Results: Compared with the results without consideration of the variation on acoustic speed, sound sources reconstructed by our method are more consistent with the model in terms of size and shape., Conclusions: The novel algorithm can be used to reconstruct the high-accuracy image of MAT-MI sound source in the sound velocity inhomogeneous media. In addition, this method is applicable to scenarios that the prior knowledge of the imaging targets is unknown. The signal acquisition time of MAT-MI in acoustically heterogeneity media is greatly reduced due to the introduction of ring transducer array into the imaging system. Therefore, our method will promote the application of MAT-MI in noninvasive early diagnosis of tumor for preclinical study., (© 2020 American Association of Physicists in Medicine.)

Bioluminescence tomography (BLT) is a molecular imaging modality which can three-dimensionally resolve the molecular processes in small animals in vivo. The ill-posedness nature of BLT problem makes its reconstruction bears nonunique solution and is sensitive to noise. In this paper, we proposed a sparse BLT reconstruction algorithm based on semigreedy method. To reduce the ill-posedness and computational cost, the optimal permissible source region was automatically chosen by using an iterative search tree. The proposed method obtained fast and stable source reconstruction from the whole body and imposed constraint without using a regularization penalty term. Numerical simulations on a mouse atlas, and in vivo mouse experiments were conducted to validate the effectiveness and potential of the 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., (Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

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., (© 2011 Optical Society of America)

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.

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.

Objective. Fluorescence molecular tomography (FMT) is a promising non-invasive optical molecular imaging technology with strong specificity and sensitivity that has great potential for preclinical and clinical studies in tumor diagnosis, drug development and therapeutic evaluation. However, the strong scattering of photons and insufficient surface measurements make it very challenging to improve the quality of FMT image reconstruction and its practical application for early tumor detection. Therefore, continuous efforts have been made to explore more effective approaches or solutions in the pursuit of high-quality FMT reconstructions. Approach. This review takes a comprehensive overview of advances in imaging methodology for FMT, mainly focusing on two critical issues in FMT reconstructions: improving the accuracy of solving the forward physical model and mitigating the ill-posed nature of the inverse problem from a methodological point of view. More importantly, numerous impressive and practical strategies and methods for improving the quality of FMT reconstruction are summarized. Notably, deep learning methods are discussed in detail to illustrate their advantages in promoting the imaging performance of FMT thanks to large datasets, the emergence of optimized algorithms and the application of innovative networks. Main results. The results demonstrate that the imaging quality of FMT can be effectively promoted by improving the accuracy of optical parameter modeling, combined with prior knowledge, and reducing dimensionality. In addition, the traditional regularization-based methods and deep neural network-based methods, especially end-to-end deep networks, can enormously alleviate the ill-posedness of the inverse problem and improve the quality of FMT image reconstruction. Significance. This review aims to illustrate a variety of effective and practical methods for the reconstruction of FMT images that may benefit future research. Furthermore, it may provide some valuable research ideas and directions for FMT in the future, and could promote, to a certain extent, the development of FMT and other methods of optical tomography. [ABSTRACT FROM AUTHOR]

Fluorescence molecular tomography (FMT), as a powerful imaging technique in preclinical research, can offer the three-dimensional distribution of biomarkers by detecting the fluorescently labelled probe noninvasively. However, because of the light scattering effect and the ill-pose of inverse problem, it is challenging to develop an efficient reconstruction method, which can provide accurate location and morphology of the fluorescence distribution. In this research, we proposed a novel adaptive Gaussian weighted Laplace prior (AGWLP) regularization method, which assumed the variance of fluorescence intensity between any two voxels had a non-linear correlation with their Gaussian distance. It utilized an adaptive Gaussian kernel parameter strategy to achieve accurate morphological reconstructions in FMT. To evaluate the performance of the AGWLP method, we conducted numerical simulation and in vivo experiments. The results were compared with fast iterative shrinkage (FIS) thresholding method, split Bregman-resolved TV (SBRTV) regularization method, and Gaussian weighted Laplace prior (GWLP) regularization method. We validated in vivo imaging results against planar fluorescence images of frozen sections. The results demonstrated that the AGWLP method achieved superior performance in both location and shape recovery of fluorescence distribution. This enabled FMT more suitable and practical for in vivo visualization of biomarkers. [ABSTRACT FROM AUTHOR]

SM5-1 is a humanized mouse monoclonal antibody, targeting an over-expressed membrane protein of approximately 230 kDa in hepatocellular carcinoma (HCC). SM5-1 can be used for target therapy in hepatocellular carinoma due to its ability of inhibiting cell growth and inducing apoptosis. However, the tumor inhibition efficacy of SM5-1 in HCC cancer treatment remains low. In this study, we synthesized SM5-1-conjugated gold nanoparticles (Au-SM5-1 NPs) and investigated their anticancer efficacy in HCC both in vitro and in vivo . The tumor inhibition rates of Au-SM5-1 NPs for subcutaneous tumor mice were 40.10% ± 4.34%, 31.37% ± 5.12%, and 30.63% ± 4.87% on day 12, 18, and 24 post-treatment as determined by bioluminescent intensity. In addition, we investigated the antitumor efficacy of Au-SM5-1 NPs in orthotopic HCC tumor models. The results showed that the inhibition rates of Au-SM5-1 NPs can reach up to 39.64% ± 4.87% on day 31 post-treatment determined by the bioluminescent intensity of the abdomen in tumor-bearing mice. Furthermore, three-dimensional reconstruction results of the orthotopic tumor revealed that Au-SM5-1 NPs significantly inhibited tumor growth compared with SM5-1 alone. Our results suggested that the developed Au-SM5-1 NPs has great potential as an antibody-based nano-drug for HCC therapy. [ABSTRACT FROM AUTHOR]

Optical molecular imaging has been rapidly developed to noninvasively visualize in vivo physiological and pathological processes involved in normal and suffering organisms at the cellular and molecular levels, in which advanced optical imaging technology and modern molecular biology are being combined to provide a state-of-the-art tool for preclinical biomedical research. Among optical molecular imaging modalities, bioluminescence tomography (BLT) has experienced considerable growth and attracted much attention in recent years for its excellent performance, unique advantages, and high cost-effectiveness. This article focuses on the genesis and development of BLT, especially for its computational methodology, imaging system, and biomedical application. An overview of the advantages and challenges of the conventional planar bioluminescence imaging technique is first described in comparison with currently available molecular imaging modalities. The imaging algorithms for inverse source reconstruction are classified and summarized according to different a priori knowledge, followed by a simple depiction of the uniqueness theorems of BLT solution. Diverse imaging systems for obtaining three-dimensional quantitative information of internal bioluminescent sources are then reviewed. The latest application examples of BLT in tumor study and drug discovery are introduced and compared with other mature imaging technologies. Finally, the paper is concluded and an attractive prospect for BLT is predicted. [ABSTRACT FROM AUTHOR]