An efficient digital pulse processing approach for identification of BGO and LSO scintillator crystals

Abstract 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.