In the reconstruction of the quantitative photoacoustic tomography (QPAT), forward models for both the optical and acoustic problems are usually needed to predict the measurement data from a guess of the distribution of the optical parameters. The diffusion approximation (DA) is the one most often employed as the optical forward model in the QPAT. However, this model usually results in predicted data deviating far from the actual measurements especially in low scattering tissues. To tackle such a problem, we propose a reconstruction method where the modeling error of the DA is modeled and considered in the framework of Bayesian inference. Experimental results show that modeling of the approximation error and considering it in the reconstruction procedure can significantly improve the reconstructed results of the QPAT.
The analysis of fluorescence molecular tomography is important for medical diagnosis and treatment. Although the quality of reconstructed results can be improved with the increasing number of measurement data, the scale of the matrices involved in the reconstruction of fluorescence molecular tomography will also become larger, which may slow down the reconstruction process. A new method is proposed where measurement data are reduced according to the rows of the Jacobian matrix and the projection residual error. To further accelerate the reconstruction process, the global inverse problem is solved with level-by-level Schur complement decomposition. Simulation results demonstrate that the speed of the reconstruction process can be improved with the proposed algorithm.
A novel shape classification method based on Hidden Markov Models (HMMs) is proposed in the paper. Instead of
characterizing points along an object contour, our method employs HMMs to model the relationship among structural
segments of the contour. Firstly, an object contour is partitioned into segments at points with zero curvature value.
Secondly, each segment is represented with structural features. Finally, a HMMs is utilized to characterize the object
contour by treating each segment as an observation of a hidden state. Promising experimental results obtained on two
popular shape datasets demonstrate that the proposed method is efficient in classifying shapes, particularly unclosed
shapes and similar shapes.
The forward problem of the fluorescent molecular tomography(FMT), which is usually described by two coupled
diffusion equations corresponding to the excitation and emission light respectively, is usually solved in a sequential
manner. However, sequential computation often limits the FMT image reconstruction speed. In this paper, a novel
parallel forward computation algorithm is proposed in conjunction with a reconstruction algorithm based on the
adaptively refined mesh, in which the priori information obtained from the other imaging modalities can be easily
incorporated in the process of mesh generation. The experiment results and comprehensive discussion given in this paper
have demonstrated that the proposed parallel forward computation and reconstruction strategies based on the adaptively
refined mesh can improve the performance of the FMT reconstruction in terms of image reconstruction speed and final
image quality significantly.
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