ABSTRACT
Objective: To quantitatively analyze the influence of 3D mean-median filter parameter on ordered subsets expectation maximization (OSEM) reconstruction images through applying 3D mean-median filter to process the projection data before reconstruction and adjusting the parameter. Methods: 3D mean-median filter was applied to process 3D phantom projection data simulated by the Analytical Simulator (ASIM). OSEM algorithm of open source tomographic image reconstruction (STIR) was used to reconstruct the projection data before and after filtering. Finally, the reconstructed image was visually and quantitatively evaluated. Results: The filter parameter K was closely related to image quality. If the Kvalue was too large, the edge preservation capability of image was poor, and the image was too smooth. If the Kvalue was too small to suppress noise, the details of image were blurred. Conclusion: The noise level and edge preservation effect of the image are very sensitive to the selection of filtering parameter K. The range of filtering parameter can be selected according to the distribution of gradient histogram. The appropriate parameter can be chosen by combining with the gradient distribution ratio, so as to remove noise and retain edge characteristic.
ABSTRACT
Even in a well-designed and controlled study, missing data occurs in almost all research. Missing data can reduce the statistical power of a study and can produce biased estimates, leading to invalid conclusions. This manuscript reviews the problems and types of missing data, along with the techniques for handling missing data. The mechanisms by which missing data occurs are illustrated, and the methods for handling the missing data are discussed. The paper concludes with recommendations for the handling of missing data.
Subject(s)
Bias , Handling, PsychologicalABSTRACT
PURPOSE: In this study we propose a block-iterative method for reconstructing Compton scattered data. This study shows that the well-known expectation maximization (EM) approach along with its accelerated version based on the ordered subsets principle can be applied to the problem of image reconstruction for Compton camera. This study also compares several methods of constructing subsets for optimal performance of our algorithms. MATERIALS AND METHODS: Three reconstruction algorithms were implemented; simple backprojection (SBP), EM, and ordered subset EM (OSEM). For OSEM, the projection data were grouped into subsets in a predefined order. Three different schemes for choosing nonoverlapping subsets were considered; scatter angle-based subsets, detector position-based subsets, and both scatter angle- and detector position-based subsets. EM and OSEM with 16 subsets were performed with 64 and 4 iterations, respectively. The performance of each algorithm was evaluated in terms of computation time and normalized mean-squared error. RESULTS: Both EM and OSEM clearly outperformed SBP in all aspects of accuracy. The OSEM with 16 subsets and 4 iterations, which is equivalent to the standard EM with 64 iterations, was approximately 14 times faster in computation time than the standard EM. In OSEM, all of the three schemes for choosing subsets yielded similar results in computation time as well as normalized mean-squared error. CONCLUSION: Our results show that the OSEM algorithm, which have proven useful in emission tomography, can also be applied to the problem of image reconstruction for Compton camera. With properly chosen subset construction methods and moderate numbers of subsets, our OSEM algorithm significantly improves the computational efficiency while keeping the original quality of the standard EM reconstruction. The OSEM algorithm with scatter angle- and detector position-based subsets is most available.