Relevance of MTF and NPS in Quantitative CT: Towards Developing a Predictable Model of Quantitative Performance

The quantification of lung nodule volume based on CT images provides valuable information for disease diagnosis and staging. However, the precision of the quantification is protocol, system, and technique dependent and needs to be evaluated for each specific case. To efficiently investigate the quantitative precision and find an optimal operating point, it is important to develop a predictive model based on basic system parameters. In this study, a Fourier-based metric, the estimability index (e') was proposed as such a predictor, and validated across a variety of imaging conditions. To first obtain the ground truth of quantitative precision, an anthropomorphic chest phantom with synthetic spherical nodules were imaged on a 64 slice CT scanner across a range of protocols (five exposure levels and two reconstruction algorithms). The volumes of nodules were quantified from the images using clinical software, with the precision of the quantification calculated for each protocol. To predict the precision, e' was calculated for each protocol based on several Fourier-based figures of merit, which modeled the characteristic of the quantitation task and the imaging condition (resolution, noise, etc.) of a particular protocol. Results showed a strong correlation (R-2=0.92) between the measured and predicted precision across all protocols, indicating e' as an effective predictor of the quantitative precision. This study provides a useful framework for quantification-oriented optimization of CT protocols.