Comparison of energy bin compression strategies for photon counting detectors

Yirong Yang, Sen Wang, Debashish Pal, Norbert J. Pelc, Adam S. Wang, Joseph Webster Stayman
2022 7th International Conference on Image Formation in X-Ray Computed Tomography  
Photon counting detectors (PCDs) with energy discrimination capabilities allow us to perform quantitative material decomposition with high spatial resolution. Although PCDs provide more spectral information than conventional energy integrating detectors (EIDs), it is more challenging for the system to transmit projection data from the detectors across the slip ring to the processing computer and store the data, due to the increased amount of data with increasing number of energy bins. To
more » ... this problem, many approaches have been proposed to compress the bin data while maintaining the image quality. In this work, we compare the performance of strategies to reduce projection data and determine the optimal choice of bin compression strategies and the number of measurements for multiple tasks. We first obtain the optimal thresholds for conventional energy bins, as determined by minimizing the Cramér-Rao lower bound (CRLB) for material decomposition tasks with a realistic silicon detector energy response. We then consider the case of reducing data from 8 native energy bins by forming weighted sums, either with binary weights or continuous weights, by minimizing the relative CRLB between the compressed measurements and the original 8 bins. We then evaluate their respective performance using Monte Carlo simulation for a head phantom. The results show that the continuous weights strategy is superior to others, with low bias and less than 10% variance penalty for 2 weighted sums, with a data reduction of 75% within a large material thickness space. The other strategies have up to 50% variance penalty compared with the original 8 bins and are less robust when there is photon starvation. With additional weighted measurements, the continuous weights method can achieve less than 1% variance penalty when reducing the 8 native energy bins to half the number of measurements. Overall, combining energy bins by forming weighted sums with continuous weights is an effective strategy for reducing data while preserving spectral information.
doi:10.1117/12.2646506 fatcat:hje7sqnph5gljfdxgwoutbyluy