Design And Implementation Of Small Animal Panel-based Positron Emission Tomography System And Performance Evaluation

Positron emission tomography(PET)is an imaging technology sensitive to biochemical process,which attracted lots of attention duo to the ability of early diagnosis of serious diseases,such as cardiovascular diseases and cancer.The PET system for small animals plays a significant role in the preclinical research.With the advantages of simple structure,easy realization,openness,and adjustability,the panel PET system for small animals with one pair or multiple pairs of panel-based detectors is widely used.This thesis is dedicated to the development of the small animal panel PET system with high resolution and high imaging quality.Normalization correction and performance evaluation for the PET system are also described in the thesis.This thesis consists of the following parts:1.Design and realization of the small animal panel-based PET imaging systemThe hardware system construction of the small animal panel-based PET system,the implementation of the system control software and the data preprocessing were accomplished.In the aspect of hardware construction,based on PET basic detection modules(BDM),a small animal panel-based PET imaging system were designed and constructed.For the control software,the author developed the integrated control software based on Qt platform.In the process of data preprocessing,the information of the location,energy and time of the incoming gamma photon was processed.Based on the energy resolution and time resolution of the system,the energy window and coincidence time window were determined.With the above information,coincidence events were obtained.2.Normalization correction for the small animal panel-based PET systemTo resolve the problem that the efficiency of each 1ine of response(LOR)of the PET system varies,the author performed the normalization correction using the component-based maximum-likelihood estimation(ML)method.In the correction process,the monte carlo(MC)Simulation method was proposed to calculate the forward projection,so that the geometric difference factor of the system was not needed to be calculated separately in the correction process,which resulted in the subsequent normalized correction relatively simple.The author also compared the ML method with the Direct Normalization method.The experiment results show that the ML method has better performance.3.Performance evaluation of the small animal panel-based PET imaging systemThe sensitivity and spatial resolution of the small animal panel-based PET system were tested.The sensitivity and spatial resolution of the panel-based PET system consisting of 2 BDM and 4 BDM were tested.The sensitivity in the center of the two system could reach 3.13% and 4.97% respectively,and the spatial resolution was 1mm.In the experiment,the component-based normalization correction method had great performance on the removal of reconstructed image artifacts.