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Influence of x-ray pulse generated from gamma spectrometers should be eliminated in applications, which typically uses pulse shape techniques between gamma and x-ray pulses. In this study, we proposed and tested several algorithms aiming to eliminate this influence. The algorithms are based on curve fitting (CF), artificial neural network (ANN), system identification, peak shape, amplitude search with curve fitting and pulse tracking methods. Gamma pulses and X-ray pulses are detected by NaI(TI) scintillator detector and Silicon lithium Si(Li) detector, respectively. The developed algorithms are tested using 32,000 total instantaneous detector events of acquired gamma pulses and 65,536 total instantaneous detector events of x-ray source. An algorithm using the least square curve fitting method is applied for differentiation between gamma and x-ray pulses. ANN is employed as a classifier for identification of extracted spectrum and Bispectrum features of gamma and x-ray pulses. A comparison between identification results due to extracted spectrum and Bispectrum features is established. System identification algorithm is then built to determine the detection system response of each radiation pulse, which includes various models to attain best fitting. These models are Auto-regressive model with external input (ARX), the linear parametric model (IV) and process models (P1D). The peak shape algorithm is also tried, which depends on the individual classification of pulse width. The amplitude search with curve fitting algorithm is implemented. Moreover, the pulse tracking algorithm is investigated for PSD between gamma and x-ray pulses. The maximum peak of contaminated pulse is tracked using a suggested peak search method. Then, pulse position is estimated using matrix method. Comparison between these algorithms is conducted based on the evaluation of light of residuals, fitting error and processing time. The results confirm that peak shape algorithm is the best one from computational speed point of view, while ANN algorithm using Bispectrum feature extraction method is the most appropriate one that yields 100% accuracy over noisy environment with longer processing time. In addition, the system identification algorithm is the optimal algorithm that achieves zero fitting error under clean environment. These proposed algorithms for PSD between gamma and x-ray pulses lead to design efficient spectrometers with optimal applicability in various environments.

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Identification and discrimination between BGO and LSO scintillators is a fundamental target for handling parallax error within positron emission tomography (PET) applications by depth of interaction. An approach is built for discrimination and identification of BGO and LSO scintillator crystals. This approach is tested using a simulated BGO and LSO pulses. A Matlab Simulink model is implemented for simulation and creation of BGO and LSO scintillation pulses. The simulated pulses depend on 22Na radiation source. The suggested approach has two different algorithms. The first algorithm uses both 1D-Walsh ordered fast Walsh-Hadamard transform (1WFWHT) and fast Chebyshev transform (FCHT) for extracting the features of crystal pulses. The optimum features are selected from 1WFWHT and FCHT using one of three optimization techniques that are binary dragonfly optimization (BDA), binary atom search optimization (BASO) and binary Harris Hawk optimization (BHHO). These optimized features are trained and tested using one of three based classifiers. These classifiers are Naive Bayes classifier (NBC), hierarchical prototype-based (HP) classifier and adaptive neuro-fuzzy inference system (ANFIS) classification. The ANFIS classifier achieves the best accuracy with all optimum (BASO, BDF and BHH) FCHT features. However, the NB classifier introduces the highest accuracy with all optimum (BASO, BDF and BHH) FWHT 1D features. The second algorithm uses the conventional neural network (CNN) for extracting the pulse features. Then, the deep neural network (DNN) is applied for training and testing of the captured pulses. The suggested algorithms are verified and compared in respect of statistical measures. The compared results confirm that the best accuracy and identification rate is accomplished using DNN algorithm. Besides, the DNN has better results compared to conventional classification techniques with optimum feature selection techniques in respect of time consumption. The proposed approach aids in the realisation of overcoming parallax inaccuracy in PET.