Empirical optimization of energy bin weights for compressing measurements with photon counting x-ray detectors

Abstract

Photon counting detectors (PCDs) with energy discrimination capabilities provide spectral information through energy binning and higher spatial resolution than conventional energy integrating detectors (EIDs). However, the projection data transmission from the detector across the slip ring to the processing computer becomes more challenging due to the increased amount of data, including multiple (e.g., 8) energy bins. In this work, we propose a projection-domain energy bin weighting method that produces two energy-weighted measurements to provide comparable spectral information as the original binned counts for material decomposition and virtual monoenergetic imaging tasks. We obtain the optimal energy bin weights by minimizing the Cramér–Rao lower bound (CRLB) ratio between the weighted measurements and that of the original binned counts and evaluate their respective material decomposition performance using Monte Carlo simulation. The experiments were conducted with realistic photon counting detector energy responses, which were not assumed to be known. Instead, only an empirical calibration using a step-wedge phantom is required, making this process extensible to any photon counting detector without prior knowledge of its energy response or the incident spectrum. The results show that the two energy-weighted measurements generated with our method can provide comparable material decomposition results with low bias and less than 20% variance penalty to that of the original binned counts for a large range of patient size, with a data reduction of 75% for a silicon detector with 8 energy bins and 60% for a CdTe detector with 5 energy bins.