Self-Organizing Map Neural Network-Based Nearest Neighbor Position Estimation Scheme for Continuous Crystal PET Detectors

作     者：Wang, Yonggang Li, Deng Lu, Xiaoming Cheng, Xinyi Wang, Liwei 

作者机构：Univ Sci & Technol China Dept Modern Phys Hefei 230026 Anhui Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON NUCLEAR SCIENCE》 (IEEE Trans Nucl Sci)

年 卷 期：2014年第61卷第5期

页      面：2446-2455页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0827[工学-核科学与技术] 

基　　金：National Natural Science Foundation of China (NSFC) [10975131  11075155  11275194] 

主　　题：Continuous crystal PET detector nearest neighbor algorithm position of interaction SOM neural network 

摘      要：Continuous crystal-based positron emission tomography (PET) detectors could be an ideal alternative for current high-resolution pixelated PET detectors if the issues of high performance gamma interaction position estimation and its real-time implementation are solved. Unfortunately, existing position estimators are not very feasible for implementation on field-programmable gate array (FPGA). In this paper, we propose a new self-organizing map neural network-based nearest neighbor (SOM-NN) positioning scheme aiming not only at providing high performance, but also at being realistic for FPGA implementation. Benefitting from the SOM feature mapping mechanism, the large set of input reference events at each calibration position is approximated by a small set of prototypes, and the computation of the nearest neighbor searching for unknown events is largely reduced. Using our experimental data, the scheme was evaluated, optimized and compared with the smoothed k-NN method. The spatial resolutions of full-width-at-half-maximum (FWHM) of both methods averaged over the center axis of the detector were obtained as 1.87 +/- 0.17 mm and 1.92 +/- 0.09 mm, respectively. The test results show that the SOM-NN scheme has an equivalent positioning performance with the smoothed k-NN method, but the amount of computation is only about one-tenth of the smoothed k-NN method. In addition, the algorithm structure of the SOM-NN scheme is more feasible for implementation on FPGA. It has the potential to realize real-time position estimation on an FPGA with a high-event processing throughput.