A neighborhood standard deviation based algorithm for generating PET crystal position maps

Positron emission tomography (PET) is typically based on 2-D array of scintillation crystals coupled to photon detector and decoded by the Anger-logic. The decoded result is a pseudo-position of the gamma interaction. A crystal position map (CPM) generated from the flood histogram is used as a crystal look-up table (CLT) to assign each pseudo-position to a specific crystal. It is crucial that the accuracy of CPMs affects the detector's spatial resolution. In this paper, we developed a neighborhood standard deviation (NSD) based algorithm for generating CPM. We first calculated the NSD of each pixel in the flood histogram including the x and y directions. NSD maps have strips whose peaks highly correspond to the valley of the flood histogram. The peaks were identified by fitting the profiles of NSD to Gaussian mixture functions using nonlinear least-square method. Using the peaks, the CPM was generated by a scan line algorithm. The proposed algorithm was applied in an animal PET system. 115 of 120 detector blocks can be automatically segmented in ~1000 s. A hot rod phantom experiment was performed, and the reconstruction results showed that the one with CPM generated by NSD based automatic method achieved higher spatial resolution than the one with CPM generated by manual segmentation. We concluded that the proposed method is fast, robust and high accuracy.