DOI-PET image reconstruction with accurate system model reducing redundancy of imaging system

Abstract

A high-performance PET scanner, which measures depth-of-interaction (DOI) information, is in progress at the National Institute of Radiological Sciences in Japan. Image reconstruction methods with accurate modeling of the system response functions have been successfully used to improve PET image quality. It is, however, difficult to apply these methods to the DOI-PET system because the dimension of DOI-PET data increases in proportion to the square of the number of DOI layers. In this paper, we propose a compressed imaging system model for DOI-PET image reconstruction, in order to reduce computational cost with keeping image quality. The basic idea of the proposed method is that the DOI-PET system is highly redundant. First, DOI-PET data are transformed into compact data so that data bins of which sensitivity functions highly correlate are combined. Then image reconstruction methods based on accurate system modeling, such as the maximum likelihood expectation maximization (NIL-EM), are applied. The proposed method was applied to simulated data for the DOI-PET operated in 2D mode. Then the trade-off between the background noise and the spatial resolution was investigated. Numerical simulation results show that the proposed method followed by ML-EM reduces computational cost effectively with keeping the advantages of the accurate system modeling and DOI information.