A Fast Accuracy Crystal Identification Method Based on Fuzzy C-Means (FCM) Clustering Algorithm for MicroPET

Abstract

A high resolution detector is being developed for our small animal position emission tomography (MicroPET). The detector unit consist of 8x8 crystal blocks, coupled to four photomultiplier tubes (PMTs). Each scintillation event is mapped in a two dimensional (2-D) position through the relative ratio of the output signals of the PMTs. Crystal Look-up table (CLT) used in ThuMicroPET scanner defines the matching relation between signal position of a detected event to a corresponding detector pixel location. It has a direct impact on imaging quality and brings significant influence to the gantry overall performance. However, the currently used method involves a lot of human interaction for CLT corrections, and cannot be implemented as a general process due to its complexity. This paper introduces a fast accuracy method based on Fuzzy C-Means (FCM) Clustering Algorithm for crystal identification. In the FCM, a cluster center and a fuzzy partition matrix of individual events in the 2-D position are defined. By iteratively updating the cluster centers and the membership grades for each event, we can move the cluster center to the right location in a short time, based on minimizing objective function that represents the distance from any given events to a cluster center weighted by its membership grade. The preliminary result shows that FCM can be used effectively in CLT construction, which significantly reduces the time, and brings excellent accuracy than we expected.