Acceleration of PET-based industrial internal defect image reconstruction using FPGA

Abstract

Positron emission tomography (PET) can be used to measure the internal defects of industrial parts. However, PET requires a long execution time of image reconstruction, which hinders its practical usage in industrial measurements. A novel parallel scheme based on field-programmable gate arrays (FPGAs) is proposed in this study to accelerate PET image reconstruction. A fast maximum-likelihood expectation-maximization iteration reconstruction algorithm with prior estimation is implemented on the FPGA. This method can achieve satisfactory PET images with limited iteration times. The resources in the FPGA are divided into several groups, and each group supports the image reconstruction for a single sinogram. Thus, several sinograms can be processed in parallel. Two internal defect detection experiments are conducted to apply the proposed method to industrial measurements. Results show that the inner structure can be detected, whereas the inner defects can be visualized. A group of 104 slice images is reconstructed in parallel on FPGAs, and the final 3D PET image of the inner defects is acquired in 10 s.