X-ray measurement model incorporating energy-correlated material variability and its application in information-theoretic system analysis

TL;DR

This paper introduces a multi-energy X-ray measurement model that accounts for material variability and energy correlations, enabling better analysis of system performance limits in imaging and sensing applications.

Contribution

It presents a novel measurement model incorporating material variability with energy correlations and derives bounds on system performance for binary classification tasks.

Findings

Analytical bounds on probability of error for X-ray systems

Demonstration of model utility through system performance analysis

Enhanced understanding of material variability effects in X-ray imaging

Abstract

Extending our prior work, we propose a multi-energy X-ray measurement model incorporating material variability with energy correlations to enable the analysis and exploration of the performance of X-ray imaging and sensing systems. Based on this measurement model, we provide analytical expressions for bounds on the probability of error, $P_e$, to quantify the performance limits of an X-ray measurement system for binary classification task. We analyze the performance of a prototypical X-ray measurement system to demonstrate the utility of our proposed material variability measurement model.