Comparison of energy bin compression strategies using a prototype silicon photon counting CT detector

Abstract

Photon counting detectors (PCDs) with energy discrimination capabilities provide numerous benefits over conventional energy integrating detectors (EIDs). PCDs enable higher spatial resolution, improved contrast-to-noise ratio, reduced beam hardening artifact, and quantitative material-selective imaging. However, the increased spatial resolution and multiple energy bins (e.g., 8 bins for a prototype photon counting CT using silicon-based PCDs) greatly increase the amount of data generated. As a consequence, projection data transmission from the detector to the processing computer becomes more challenging due to the limited bandwidth of the slip ring. In this work, we compare the performance of four projection-domain energy bin compression strategies: conventional bins, summed bins, binary weights, and continuous weights, using the raw projection data from a prototype photon counting CT using silicon-based PCDs. We reduce the 8 energy bins of the projection data from the prototype silicon-based PCDs to 2 or 3 virtual measurements using the phantom independent and globally applicable bin compression strategies, which were each optimized by minimizing the Cramér–Rao lower bound (CRLB) of the virtual measurements using only material decomposition calibration data over a predefined material space, and no other a priori knowledge. We evaluate the performance of the above 4 bin compression strategies in reconstructed images of a Catphan700 phantom. The results show that the 2 measurements generated with continuous weights can provide comparable material decomposition (MD) and virtual monoenergetic images (VMI) that exhibit low bias- and near zero variance-penalty, to that of the original binned counts, with a data reduction of 75%. On the other hand, neither conventional bins, summed bins, nor binary weights can provide comparable results versus continuous weights, even if we use 3 virtual measurements. We also conclude that to achieve low bias and variance in MD and VMI images with only two measurements, it is necessary to first measure as many energy bins as possible, then use continuous weights to compress the binned data.