In Physics in medicine and biology
OBJECTIVE : Pixelated semiconductor detectors such as CdTe and CZT sensors suffer spatial resolution and spectral performance degradation induced by charge-sharing effects. It is critical to enhance the detector property through recovering the energy-deposition and position estimation.
APPROACH : In this work, we proposed a Fully-Connected-Neural-Network (FCNN)-based charge-sharing reconstruction algorithm to correct the charge-loss and estimate the sub-pixel position for every multi-pixel charge-sharing event.
MAIN RESULTS : Evident energy resolution improvement can be observed by comparing the spectrum produced by a simple charge-sharing addition method and the proposed energy correction methods. We also demonstrate that sub-pixel resolution can be achieved in projections obtained with a small pinhole collimator and an innovative micro-ring collimator.
SIGNIFICANCE : These achievements are crucial for multiple-tracer SPECT imaging applications, and for other semiconductor detector-based imaging modalities.
Yang Can, Zannoni Elena Maria, Meng Ling-Jian
2022-Nov-29
CdTe/CZT detector, Fully Connected Neural Networks, charge-sharing, energy correction, machine learning, semiconductor detector
