High-Sensitivity Iodine Imaging by Combining Spectral CT Technologies

TL;DR

This paper introduces a model-driven framework for optimizing spectral CT system design to enhance iodine detection sensitivity, combining source kVp control and k-edge filtration, resulting in improved material separation and reduced noise.

Contribution

It presents a novel optimization framework for spectral CT design that combines multiple technologies to improve iodine imaging sensitivity and accuracy.

Findings

Hybrid spectral CT system outperforms single-technique designs in material separation.

Optimized system reduces noise and cross-material correlations.

Experimental results validate the effectiveness of the hybrid design.

Abstract

Spectral CT offers enhanced material discrimination over single-energy systems and enables quantitative estimation of basis material density images. Water/iodine decomposition in contrast-enhanced CT is one of the most widespread applications of this technology in the clinic. However, low concentrations of iodine can be difficult to estimate accurately, limiting potential clinical applications and/or raising injected contrast agent requirements. We seek high-sensitivity spectral CT system designs which minimize noise in water/iodine density estimates. In this work, we present a model-driven framework for spectral CT system design optimization to maximize material separability. We apply this tool to optimize the sensitivity spectra on a spectral CT test bench using a hybrid design which combines source kVp control and k-edge filtration. Following design optimization, we scanned a water/iodine phantom with the hybrid spectral CT system and performed dose-normalized comparisons to two single-technique designs which use only kVp control or only kedge filtration. The material decomposition results show that the hybrid system reduces both standard deviation and crossmaterial noise correlations compared to the designs where the constituent technologies are used individually.