4D (X,Y,Z,E) Event Parameter Estimation in a Hybrid Modular Gamma Camera

• Published in: 2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and International Symposium on Room-Temperature Semiconductor Detectors (NSS MIC RTSD) p. 1
• Main Authors: Doty, K. J., Kupinski, M. A., King, M. A., Kuo, P. H., Furenlid, L. R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 04.11.2023
• Subjects: Maximum likelihood detection; Maximum likelihood estimation; Parameter estimation; Photomultipliers; Semiconductor detectors; Shape; Three-dimensional displays
• ISSN: 2577-0829
• DOI: 10.1109/NSSMICRTSD49126.2023.10338215

We are developing a 4D maximum-likelihood gamma-ray parameter-estimation algorithm that will be used for the hybrid modular gamma cameras designed for AdaptiSPECT-C that use a combination of silicon photomultipliers (SiPMs) and photomultiplier tubes (PMTs) for optical readout. Energy estimation is usually based on the sum of the signals of the light sensors in the camera. This works when all of the light sensors are of the same size and shape. Due to the hybrid combination of light sensors in AdaptiSPECT-C, energy estimation based on the sum of the signals is no longer possible due to strong dependence on the position and depth-of-interaction (DOI). In this work, we show, using 3D simulated MDRFs at a reference energy, that energy estimation is dependent on 3D position estimation. Therefore, a fast and reliable maximum-likelihood x,y,z and energy parameter estimation algorithm is needed for these cameras. We investigate whether the energy should be estimated simultaneously with the 3D position in a 4D contracting grid or whether it is equally effective to alternate between contracting 3D position and 1D energy grids.