Estimating detectability index in vivo: development and validation of an automated methodology

Abstract

This study's purpose was to develop and validate a method to estimate patient-specific detectability indices directly from patients' CT images (i.e., <i>in vivo</i>). The method extracts noise power spectrum (NPS) and modulation transfer function (MTF) resolution properties from each patient's CT series based on previously validated techniques. These are combined with a reference task function (10-mm disk lesion with [Formula: see text] HU contrast) to estimate detectability indices for a nonprewhitening matched filter observer model. This method was applied to CT data from a previous study in which diagnostic performance of 16 readers was measured for the task of detecting subtle, hypoattenuating liver lesions ([Formula: see text]), using a two-alternative-forced-choice (2AFC) method, over six dose levels and two reconstruction algorithms. <i>In vivo</i> detectability indices were estimated and compared to the human readers' binary 2AFC outcomes using a generalized linear mixed-effects statistical model. The results of this modeling showed that the <i>in vivo</i> detectability indices were strongly related to 2AFC outcomes ([Formula: see text]). Linear comparison between human-detection accuracy and model-predicted detection accuracy (for like conditions) resulted in Pearson and Spearman correlation coefficients exceeding 0.84. These results suggest the potential utility of using <i>in vivo</i> estimates of a detectability index for an automated image quality tracking system that could be implemented clinically.