Cerenkov luminescence tomography (CLT) is a promising imaging modality in the field of optical molecular imaging (OMI), which successfully bridges the OMI and tradition nuclear medical imaging and provides the location and quantitative analysis of the distribution of radionuclide probes inside the biological objects. As the CLT is an inherent highly ill-posed inverse problem, it is still a challenge to obtain an accurate reconstruction result. Here, we proposed a novel reconstruction framework based on stacking denoising autoencoders (SDAE), which serve as one famous structure of the artificial neural network (ANN). In our framework, the initial permission region is the whole domain and then a traditional reconstruction algorithm is used to reconstruct each node's energy. Then these nodes are clustered into two regions: permission region and non-permission region, and the permission region is used to start a new reconstruction loop where a new result can be obtained. The procedures above are repeated before the result meets the stop conditions. The numerical simulation experiments, physical phantom experiments and in vivo experiments are all carried out to validate the feasibility and potential of our framework. Results demonstrate that the proposed framework can indeed achieve a good performance in CLT reconstruction. INDEX TERMS Cerenkov luminescence tomography, optical molecular imaging, reconstruction algorithm, stacking denoising autoencoders.

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s: According to index selection principle, and considering the process of selection as well as the characteristics of the architecture enterprise, this paper establishes a set of comparatively rational index appraisal system. Based on method of primary component analysis, we obtain the components from credit appraisal index of 24 architecture enterprises. It singles out the GA-BP Neural Network as the credit appraisal method of architecture enterprise, sets up a credit appraisal model of architecture enterprise, and finally verifies the practical and scientific attribute of the model.

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Based on the Pareto inferior sorting and analytic hierarchy process (AHP), a kind of multiobjective evaluation index system for university teachers' evaluation is constructed. The system introduces in inferior sorting from the theory of multiple objective, using analytic hierarchy process in the system of the second and third floors index, and using an inferior sorting method in a layer of index (subgoal layer) to choose winners, which can ensure that the object does not inferior to others. In the empirical analysis, compared with the traditional weighted method, multi-objective evaluation index system can be more objectively review object for comprehensive evaluation.