Task-based weights for photon counting spectral x-ray imaging
Abstract
PURPOSE: To develop a framework for taking the spatial frequency composition of an imaging task into account when determining optimal bin weight factors for photon counting energy sensitive x-ray systems. A second purpose of the investigation is to evaluate the possible improvement compared to using pixel based weights.
METHODS: The Fourier based approach of imaging performance and detectability index d' is applied to pulse height discriminating photon counting systems. The dependency of d' on the bin weight factors is made explicit, taking into account both differences in signal and noise transfer characteristics across bins and the spatial frequency dependency of interbin correlations from reabsorbed scatter. Using a simplified model of a specific silicon detector, d' values for a high and a low frequency imaging task are determined for optimal weights and compared to pixel based weights.
RESULTS: The method successfully identifies bins where a large point spread function degrades detection of high spatial frequency targets. The method is also successful in determining how to downweigh highly correlated bins. Quantitative predictions for the simplified silicon detector model indicate that improvements in the detectability index when applying task-based weights instead of pixel based weights are small for high frequency targets, but could be in excess of 10% for low frequency tasks where scatter-induced correlation otherwise degrade detectability.
CONCLUSIONS: The proposed…
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.