A Sub-Sampling Approach for Data Acquisition in Gamma Ray Emission Tomography

State of the art data acquisition systems for small animal imaging gamma ray detectors often rely on free running Analog to Digital Converters (ADCs) and high density Field Programmable Gate Arrays (FPGA) devices for digital signal processing. In this work, a sub-sampling acquisition approach, which exploits a priori information regarding the shape of the obtained detector pulses is proposed. Output pulses shape depends on the response of the scintillation crystal, photodetector's properties and amplifier/shaper operation. Using these known characteristics of the detector pulses prior to digitization, one can model the voltage pulse derived from the shaper (a low-pass filter, last in the front-end electronics chain), in order to reduce the desirable sampling rate of ADCs. Fitting with a small number of measurements, pulse shape estimation is then feasible. In particular, the proposed sub-sampling acquisition approach relies on a bi-exponential modeling of the pulse shape. We show that the properties of the pulse that are relevant for Single Photon Emission Computed Tomography (SPECT) event detection (i.e., position and energy) can be calculated by collecting just a small fraction of the number of samples usually collected in data acquisition systems used so far. Compared to the standard digitization process, the proposed sub-sampling approach allows the use of free running ADCs with sampling rate reduced by a factor of 5. Two small detectors consisting of Cerium doped Gadolinium Aluminum Gallium Garnet (Gd
₃
Al
₂
Ga
₃
O
₁₂
: Ce or GAGG:Ce) pixelated arrays (array elements: 2 × 2 × 5 mm
³
and 1 × 1 × 10 mm
³
respectively) coupled to a Position Sensitive Photomultiplier Tube (PSPMT) were used for experimental evaluation. The two detectors were used to obtain raw images and energy histograms under 140 keV and 661.7 keV irradiation respectively. The sub-sampling acquisition technique (10 MHz sampling rate) was compared with a standard acquisition method (52 MHz sampling rate), in terms of energy resolution and image signal to noise ratio for both gamma ray energies. The Levenberg-Marquardt (LM) non-linear least-squares algorithm was used, in post processing, in order to fit the acquired data with the proposed model. The results showed that analog pulses prior to digitization are being estimated with high accuracy after fitting with the bi-exponential model.