Scintillator-based gamma-ray detectors convert gamma-ray photons into a burst of scintillation photons, and then into a pulse-shaped electrical signal. By digitizing the pulse waveform, analyses that require information about the shape of the pulse can be performed, such as pulse-shape discrimination, pile-up detection and maximum- likelihood event-parameter estimation of position, energy and time. We have developed an analog-to-digital conversion (ADC) method that hugely reduces the complexity of the data-acquisition (DAQ) system while retaining pulse-shape information, and increases the amount of information that can be extracted from detected gamma rays compared to analog methods. The new DAQ system is based on a modified 2-bit sigma-delta modulator (SDM), in which the possible outputs (00, 01, 10 and 11) are decoded in such a way that they don’t necessarily maintain a linear relationship between them. This makes it possible to optimize the SDM algorithm for different characteristic pulse shapes in order to extract as much information as possible. The optimization method that we present in this work is scintillation-crystal specific, but the use of the ADC method is not limited to gamma-ray detection.
We have proposed a novel edge-readout detector design for PET, which can easily provide sub-millimeter in-plane spatial resolution together with DOI information with resolution determined by the thickness of the crystal layers. Multiple factors that can potentially affect the coincidence resolving time (CRT) have been studied in this work. The result indicates there is a strong correlation between the coincidence resolving time and the parameters of a constant fraction discriminator (CFD), while the correlation between CRT and other factors such as inclusion of optical barriers, locations of gamma-ray interactions in the detector and light-coupling material’s refractive index (between crystal and SiPM’s silicon substrate) is weak. With optimum CFD parameters, a 200-ps CTR can be achieved.
In PET imaging, the information needed to form an image is obtained from the detection of pairs of gamma-ray photons emitted by electron-positron annihilations. An optimal timing resolution allows the system to include time-of-flight (TOF) information, which improves image quality. The two methods to approach timing estimation are analog processing and digital captured waveform analysis. In digital analysis, there is a trade-off between the amount of data acquired and the timing resolution of a detector. In order to develop an efficient data acquisition system, we want to minimize the number of digital samples by acquiring the samples that contains the most information for timing estimation. We developed a simulation package to perform Fisher information analysis on the waveform samples in order to quantify the timing information conveyed by segments of the waveform. The diagonal components of the inverse of the Fisher information matrix set the bound that establishes the Cramér-Rao inequality on the variance of an unbiased estimator. The Maximum-Likelihood (ML) estimator is unbiased and asymptotically achieves the Cramér-Rao lower bound; for this reason, the ML estimator is ideal for performing timing estimation and extracting information as described by Fisher information analysis. This document explains the simulation of the waveforms, ML estimation method, Fisher information analysis and the calculation of the Cramér-Rao lower bound, for different lengths of the pulse. The results show that the timing resolution approaches a limit using just a segment of the waveform and there are parts of the pulse that are redundant information. The yields of this work will be used to build an efficient data acquisition (DAQ) system that will acquire less amount of data, and therefore, the complexity and cost of the DAQ system will be reduced.