Crystal Identification Based on Recursive-Least-Squares and Least-Mean-Squares Auto-Regressive Models for Small Animal Pet

Most positron emission tomography (PET) scanners still partly rely on analog processing to sort out events from the PET detector front-end. Recent all-digital architectures enable the use of more complex algorithms to solve common problems in PET scanners, such as crystal identification and parallax error. Auto-regressive exogeneous variable (ARX) algorithms were shown to be among the most powerful methods of crystal identification by pulse shape discrimination (PSD) for parallax mitigation or resolution improvement with phoswich detectors. Although ARX algorithms achieve a nearly 100% discrimination accuracy even in a noisy environment, such methods are computationally expensive and can hardly be implemented in a real time digital PET system. A crystal identification method based on adaptive filter theory using an auto-regressive (AR) model is proposed to enable real time crystal identification in a noisy environment.