In SPECT/PET, the maximum-likelihood expectation-maximization (ML-EM) algorithm is getting more attention as the speed of computers increases. This is because it can incorporate various physical aspects into the reconstruction process leading to a more accurate reconstruction than other analytical methods such as filtered-backprojection algorithms. However, the convergence rate of the ML-EM algorithm is very slow. Several methods have been developed to speed it up, such as the ordered-subset expectation-maximization (OS-EM) algorithm. Even though OS-type algorithms can bring about significant acceleration in the iterative reconstruction, it is generally believed that ML-EM produces better images, in terms of statistical noise in the reconstruction. In this paper, we present an accelerated ML-EM algorithm with bigger step size and show its convergence characteristics in terms of variance noise and log-likelihood values. We also show some advantages of our method over other accelerating methods using additive forms.
This paper proposes a new iterative reconstruction algorithm for transmission tomography and compares this algorithm with several other methods. The new algorithm is simple and resembles the emission ML-EM algorithm in form. Due to its simplicity, it is easy to implement and fast to compute a new update at each iteration. The algorithm also always guarantees non-negative solutions. Evaluations are performed using simulation studies and real phantom data. Comparisons with other algorithms such as convex, gradient, and logMLEM show that the proposed algorithm is as good as others and performs better in some cases.
-In this study, physiological status of locomotive engineers were measured through EEG, ECG, EDA, PPG and respiration signals from 6 subjects to evaluate their arousal status during train operating. Existence of tunnels and mechanical vibration of train using 3-axes acceleration sensors were recorded simultaneously and were correlated with operator's physiological status. As the result of the analyzed subjects' physiological signals, mean SCR was increased in the section where more body movement is required. The RR interval was decreased before and after train stop due to the higher level of mental tension. The intensity of beta wave of EEG was found to be higher before and after train stop and tunnel section due to the increased mental arousal and tension. Therefore, it is expected that the outcomes of the physiological signals explored in this study can be utilized as the quantitative assessment methods for the arousal status to be used for sleepiness prevention system for vehicles operators which can greatly contribute to public transportation system safety.
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