Inter-crystal scatter identification for a depth-sensitive detector using support vector machine for small animal positron emission tomography

In a conventional positron emission tomography (PET) detector, detected events are projected onto a 2D position histogram by an Anger calculation for crystal identification. However, the measured histogram is affected by inter-crystal scatterings (ICS) which occur in the entire detector. Peaks which are projected for each crystal in the histogram are blurred, and this causes ICS mispositioning. A depth-of-interaction (DOI) detector has been developed for the small animal PET scanner jPET-RD. This DOI detector uses 32*32 crystals with four layers and a 256-channel multi-anode flat panel photomultiplier tube (FP-PMT) which was developed by Hamamatsu Photonics K.K. Each crystal element is 1.45*1.45*4.5mm3. The FP-PMT has a large detective area (49*49mm2) and a small anode pitch (3.04 mm). Therefore, the FP-PMT can extensively trace the behavior of incident g rays in the crystals including ICS event. We, therefore, propose a novel method for ICS estimation using a statistical pattern recognition algorithm based on a support vector machine (SVM). In this study, we applied the SVM for discriminating photoelectric events from ICS events generated from multiple-anode outputs. The SVM was trained by uniform irradiation events generated from a detector simulator using a Monte Carlo calculation. The success rate for ICS event identification is about 78% for non-training data. The SVM can achieve a true subtraction of ICS events from measured events, and it is also useful for random correction in PET