@article{oai:repo.qst.go.jp:00044796,
 author = {Yoshida, Eiji and Kitamura, Keishi and Kimura, Yuichi and Nishikido, Fumihiko and Shibuya, Kengo and Yamaya, Taiga and Murayama, Hideo and 吉田 英治 and 北村 圭司 and 木村 裕一 and 錦戸 文彦 and 澁谷 憲悟 and 山谷 泰賀 and 村山 秀雄},
 issue = {1/2},
 journal = {Nuclear Instruments & Methods in Physics Research Section A},
 month = {Feb},
 note = {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.},
 pages = {243--246},
 title = {Inter-crystal scatter identification for a depth-sensitive detector using support vector machine for small animal positron emission tomography},
 volume = {571},
 year = {2007}
}