Tutorial on X-ray photon counting detector characterization
Abstract
BACKGROUND: Recent advances in photon counting detection technology have led to significant research interest in X-ray imaging.
OBJECTIVE: As a tutorial level review, this paper covers a wide range of aspects related to X-ray photon counting detector characterization.
METHODS: The tutorial begins with a detailed description of the working principle and operating modes of a pixelated X-ray photon counting detector with basic architecture and detection mechanism. Currently available methods and techniques for charactering major aspects including energy response, noise floor, energy resolution, count rate performance (detector efficiency), and charge sharing effect of photon counting detectors are comprehensively reviewed. Other characterization aspects such as point spread function (PSF), line spread function (LSF), contrast transfer function (CTF), modulation transfer function (MTF), noise power spectrum (NPS), detective quantum efficiency (DQE), bias voltage, radiation damage, and polarization effect are also remarked.
RESULTS: A cadmium telluride (CdTe) pixelated photon counting detector is employed for part of the characterization demonstration and the results are presented.
CONCLUSIONS: This review can serve as a tutorial for X-ray imaging researchers and investigators to understand, operate, characterize, and optimize photon counting detectors for a variety of applications.
Read the paper
Related studies
- Developing high safety Li-metal anodes for future high-energy Li-metal batteries: strategies and perspectives
- Structure and Energy Transfer in Photosystems of Oxygenic Photosynthesis
- Ground- and Space-Based Gamma-Ray Astronomy
- Energy Functions in De Novo Protein Design: Current Challenges and Future Prospects
- Electronic structure of ions and molecules in solution: a view from modern soft X-ray spectroscopies
- Arising 2D Perovskites for Ionizing Radiation Detection
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.