Development of a phantom-based methodology for the assessment of quantification performance in CT
The quantification of lung nodule volume from CT images provides valuable information for cancer diagnosis and staging. However, the usefulness of quantification depends on its precision. Direct assessment of the volume quantification precision involves multiple steps and can become intractable for a multiplicity of protocols. To assess quantification precision efficiently, we developed a prediction model, named the estimability index (e'). e' provides a prediction of precision based on the characteristics of image noise and resolution, the nodule being quantified, and the segmentation software. It was further calibrated against empirical precision for 45 protocols of various reconstruction algorithms, slice thickness, and dose level. Results showed a strong correlation established between e' and the empirical precision across all 45 protocols, demonstrating e' as an effective surrogate of quantification precision. This study provides a useful framework for the optimization of CT protocols in terms of quantification precision. It also enables fast assessment of protocol compliance in terms of precision for biomarker quantification. © 2013 SPIE.
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International Standard Book Number 13 (ISBN-13)
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