We show how the spectral imaging framework should be modified to account for a high fraction of Compton interactions in low Z detector materials such as silicon. Using this framework, where deposited energies differ from actual photon energies, we compare the performance of a silicon strip detector, including the influence of scatter inside the detector and charge sharing but disregarding signal pileup, with an ideal energy integrating detector. We show that although the detection efficiency for silicon rapidly drops for the acceleration voltages encountered in clinical computed tomography practice, silicon detectors could perform on a par with ideal energy integrating detectors for routine imaging tasks. The use of spectrally sensitive detectors opens up the possibility for decomposition techniques such as k-edge imaging, and we show that the proposed modification of the spectral imaging framework is beneficial for such imaging tasks.

The introduction of photon-counting detectors is expected to be the next major breakthrough in clinical x-ray computed tomography (CT). During the last decade, there has been considerable research activity in the field of photon-counting CT, in terms of both hardware development and theoretical understanding of the factors affecting image quality. In this article, we review the recent progress in this field with the intent of highlighting the relationship between detector design considerations and the resulting image quality. We discuss detector design choices such as converter material, pixel size, and readout electronics design, and then elucidate their impact on detector performance in terms of dose efficiency, spatial resolution, and energy resolution. Furthermore, we give an overview of data processing, reconstruction methods and metrics of imaging performance; outline clinical applications; and discuss potential future developments.

Contrast-enhanced spectral mammography is feasible and beneficial with the current system, and there is room for additional improvements. Inclusion of anatomical noise is essential for optimizing spectral imaging systems.