Crystal Identification in Positron Emission Tomography Using Probabilistic Graphical Models

Abstract

Determining the location of crystals within detector arrays (position profile) is a crucial part of system tuning in Positron Emission Tomography (PET) scanners. It provides a basis for the mapping of detected events and the most probable crystal location to assign the event. Accurate assignments are crucial for proper coincidence event processing and reconstructed image resolution. In high resolution imaging systems, image resolution is of primary importance and proper position profiles are a critical part of system tuning. The High Resolution Research Tomograph (HRRT) PET scanner is composed of 936 detector blocks each with 64 dual layer crystals arranged in $8 \times8$ grids. Significant engineer time is spent fixing errors in the automated position profile estimation software used for system tuning. We have developed a probabilistic approach to position profile estimation and applied it to the HRRT PET system. Our approach is composed of a segmentation model for crosstalk filtering, a prior over valid position profile configurations, and a grid partitioning algorithm for crystal location finding. Our model outperforms the manufacturer supplied position profile estimation software, yielding a 39% decrease in mean squared error rate as compared to a gold standard configuration in the HRRT, while being a general solution applicable to many detector array configurations.