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Photoacoustic imaging is a noninvasive biomedical imaging modality for visualizing the internal structure and function of soft tissues. In this paper we present a combined iterative reconstruction algorithm to recover the distribution of optical absorption coefficients. Such a combination of the simultaneous algebraic reconstruction technique (SART) algorithm and the filtered back projection (FBP) algorithm bears the promise to improve the image quality of fast but not accurate. The different between the present proposal and traditional ART algorithm is that we computer a full error image with FBP, which is used to projection differences between original and reconstruction image, and to update the solution in each iteration step. The combined iterative reconstruction algorithm works well in rectification of the measurement and converges quickly onto an accurate estimate of the distribution of absolute absorption coefficients. Numerical simulations demonstrate that the proposed algorithm effectively reduces the artifacts and blurs, and yields better quality of reconstruction image than that with FBP algorithm in terms of both accuracy and robustness to noise.

Solution with adjustable sparsity to tomographic imaging of Cerenkov photons is presented in this work. The sparsity of radionuclides' distribution in tissues is an objective but unknown fact, and the inverse model of qualitative data is an ill-posed problem. Based on the optimization technique, the uniqueness of numerical solution to the ill-conditioned compact operator can be guaranteed by use of sparse regularization with the approximate message-passing (AMP) method. After absorbing formulations with the AMP, we analyzed the behavior of the hard thresholding operator. Iteratively numerical solutions were used to approximate the real light source by assuming the number of non-zero solution in manual mode. This modified AMP algorithm was performed in numerical simulation and physical experiments with 2-[18F]fluoro-2-deoxy-D-glucose. Experimental results indicated that the proposed method was a kind of low-complexity iterative thresholding algorithms for reconstructing 3D sparse distribution from a small set of optical measurements.

structural and functional imaging. Image reconstruction of PAI requires the solution of an inverse source problem, where the source represents the optical energy absorption distribution in the object. PAI in spherical or circular geometry gives good image resolution yet is slow in signal acquisition and image formation. Reducing the number of detection angles can ameliorate such issues. Besides, it is almost impossible to cover the entire surface of tissue. This will restrict it in the medical application with incomplete projection data. To resolve such limiting factors, in this thesis, a preconditioned conjugate gradient method is applied to the normal equations (PCCGNR method) for reconstructing the absorption distribution. Under the common assumption, a zero-mean Gaussian noise is added to the projection signals and a computer simulated has been used for the evaluation. This algorithm works well in rectification of the measurement and converges quickly onto an accurate estimate of the distribution of absolute absorption. It not only runs much faster than the FBP algorithm, but also shows stronger robustness in that it provides better image quality with detection data. We observed that diagonal preconditioners offer some improvement in convergence rate for image reconstruction, and reconstructed image preconditioning with = 0 (diagonal scaling) is closer to the true image than with > 0 . In addition, a physical experiment that will be done with our experiment equipment system further demonstrates the potential of the proposed algorithm in practical applications.

Among many optical molecular imaging modalities, bioluminescence imaging (BLI) has more and more wide application in tumor detection and evaluation of pharmacodynamics, toxicity, pharmacokinetics because of its noninvasive molecular and cellular level detection ability, high sensitivity and low cost in comparison with other imaging technologies. However, BLI can not present the accurate location and intensity of the inner bioluminescence sources such as in the bone, liver or lung etc. Bioluminescent tomography (BLT) shows its advantage in determining the bioluminescence source distribution inside a small animal or phantom. Considering the deficiency of two-dimensional imaging modality, we developed three-dimensional tomography to reconstruct the information of the bioluminescence source distribution in transgenic mOC-Luc mice bone with the boundary measured data. In this paper, to study the osteocalcin (OC) accumulation in transgenic mOC-Luc mice bone, a BLT reconstruction method based on multilevel adaptive finite element (FEM) algorithm was used for localizing and quantifying multi bioluminescence sources. Optical and anatomical information of the tissues are incorporated as a priori knowledge in this method, which can reduce the ill-posedness of BLT. The data was acquired by the dual modality BLT and Micro CT prototype system that was developed by us. Through temperature control and absolute intensity calibration, a relative accurate intensity can be calculated. The location of the OC accumulation was reconstructed, which was coherent with the principle of bone differentiation. This result also was testified by ex vivo experiment in the black 96-plate well using the BLI system and the chemiluminescence apparatus.

As a newly emerged optical imaging method, fluorescence molecular imaging technique has been receiving increasing attention for its ability of non-invasive visualization of the cellular and molecular activities. However, as a kind of background noise, autofluorescence is a major disturbing factor in fluorescence molecular imaging. In this paper, we proposed a novel method to eliminate autofluorescence of small animals. The method is based on the fact that most autofluorescent signal has a broad excitation and emission spectrum, whereas specific fluorescent probe has a narrow one. First, two fluorescent images are obtained at two different excitation wavelengths. Then we divide the two obtained fluorescent images into blocks with the size of 8×8 pixel. The two blocks from the same position of the two different images respectively constitute a block pair. The ratio of one block's summation of total pixel value to that of ther other block belonging to the same block pair is calculated. After that, we classify all block pairs into fluorescent and nonfluorescent ones by ratio. The former are considered to be actual fluorescent regions. In next step, we adopt an adaptive cluster analysis method to classify all fluorescent block pairs into multiple interest regions. A general centroid algorithm is then applied to locate the center of each interest regions. We recover the fluorescent interest regions using flood filling algorithm. Finally, we choose a GFP-transfected tumor mouse model and a GFP-transplanted mouse skin model to validate our algorithm.

Fluorescence molecular tomography (FMT) has become a promising imaging modality for in vivo small animal molecular imaging, and has many successful applications. This is partly due to the wealth of the fluorescent probes. By labeling the regions of interest with fluorescent probes, FMT can achieve non-invasive investigation of the biological process by localizing the targeted probes based on certain inverse mathematical models. However, FMT is usually an illposed problem, and some form of regularization should be included to stabilize the problem, which can be considered as the a priori information of the fluorescent probe bio-distribution. When FMT is used for the early detection of tumors, an important characteristic is the sparsity of the fluorescent sources. This is because tumors are usually very small and sparse at this stage. Considering this, general sparsity-promoting Lp-norm regularization is utilized in this paper. The iterated shrinkage based reconstruction method is adopted to solve the general Lp regularization problem. However, the original iterated shrinkage method is proved to have a linear convergence rate, and a large number of iterations are needed to obtain satisfactory results. In this paper, an improved iterated shrinkage based FMT reconstruction algorithm is proposed. By using the solutions from two previous iterations to determine the current solution, the convergence rate can be greatly increased. Heterogeneous simulation experiment shows that the proposed method can obtain comparable results with greatly reduced number of iterations compared with the original iterated shrinkage based method, which makes it a practical reconstruction algorithm.

Bioluminescence tomography (BLT) is a novel optical molecular imaging (MI) modality. It can reconstruct the inner bioluminescent light source distribution, according to the surface light distribution. The trust region method (TRM) can overcome the ill-posedness of BLT for its regularization property. As there exists a "TRUST" function that can solve the trust region subproblem in Matlab and Matlab's powerful matrix operation ability suited for TRM, the TRM is implemented in Matlab. Then the Matlab code of TRM is transformed into a dynamic linked library (DDL) and mixed together with the C++ code of the adaptive finite element (AFE) framework, using the mixed-language programming technology (MLPT). There are two main advantages of the MLPT. The first is taking advantages of all the participated programming languages. The second is time efficient. The usual way of transferring data between programmes written in different programming languages is to write the data first into files that are stored in the hard discs in one programme, and then read the files from another programme. Besides wasting time on writing and reading, it is difficult to keep the precision of the data. The DLL based MLPT can eliminate the need of installing code compilers in the platform running the software. Furthermore, in DLL, the code is implemented in C/C++ with high time efficiency, while the code in Matlab remains relatively low time efficiency. Finally, a numerical experiment is carried out to show MLPT's usage in the source reconstruction procedure of BLT, using the MLPT based on DLL.