Crystal identification based on recursive-least-squares and least-mean-squares autoregressive 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 moving-average (ARMAX) algorithms are among the most powerful methods for parallax mitigation in phoswich detectors. Although ARMAX achieves excellent discrimination accuracy even with noisy data, 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. Preliminary results based on MatLab simulation demonstrate a high discrimination accuracy (~95%) for phoswich LSO-LYSO crystals (with 40 and 51 ns decay time constants) and nearly 99% for BGO-LSO, including Compton photons