WE‐G‐110‐02: A Cascaded Systems Model for Imaging Performance and Task‐Based Optimization in Dual‐Energy Cone‐Beam CT

Abstract

Purpose: Optimization of dual‐energy (DE) tomographic imaging performance is challenged by the vast number of parameters to be considered. This work establishes a quantitative framework for theoretical analysis of noise‐power spectrum (NPS) and task‐based detectability in DE cone‐beam CT (DE‐CBCT). The model is compared to experimental results and applied to task‐based optimization of dual‐kVp selection and minimization of radiation dose. Methods: The model combines established methods for NPS propagation in DE imaging and CBCT, yielding a new framework for optimization of the DE‐CBCT imaging chain. The resulting DE‐CBCT NPS and noise‐equivalent quanta (NEQ) were derived to compute the detectability index for a variety of tasks in contrast‐enhanced musculoskeletal imaging. Theoretical calculations were validated against experimental measurements on a DE‐CBCT bench simulating the geometry of a CBCT prototype under development for musculoskeletal radiology. Optimization parameters included kVp pair, added filtration, dose allocation, etc. Results: The model provided tremendous insight in competing factors of DE‐CBCT performance. DE contrast (e.g., signal difference in 1 mm Ca or iodine to soft‐tissue) favored wide energy separation (e.g., [50/140] kVp) whereas NEQ degraded with kVp (e.g., maximized within the range considered at [65/100] kVp, with 0.6 mm Ag added to the latter). Detectability index quantitatively factored such tradeoffs in contrast and spatial‐frequency‐dependent noise along with the dose and imaging task — e.g., optimum at [65/105] kVp for a delta‐function iodine detection task. The model provided a quantitative guide to optimizing a broad scope of factors that would be difficult to consider through experimentation alone. Conclusions: A task‐based cascaded systems model establishes a new, quantitative framework for systematic optimization of DE‐CBCT imaging performance. The model combines established models shown to be of value in DE imaging and CBCT and should provide a valuable foundation for the development, optimization, and translation of high‐quality DE‐CBCT imaging techniques.This work was supported by the National Institutes of Health Grant No. R01‐CA‐112163.