11 December 2017 Estimating detectability index in vivo: development and validation of an automated methodology
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This study’s purpose was to develop and validate a method to estimate patient-specific detectability indices directly from patients’ CT images (i.e., in vivo). 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 15 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 ( N = 105 ), using a two-alternative-forced-choice (2AFC) method, over six dose levels and two reconstruction algorithms. In vivo 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 in vivo detectability indices were strongly related to 2AFC outcomes ( p < 0.05 ). 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 in vivo estimates of a detectability index for an automated image quality tracking system that could be implemented clinically.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Taylor Brunton Smith, Justin B. Solomon, Ehsan Samei, "Estimating detectability index in vivo: development and validation of an automated methodology," Journal of Medical Imaging 5(3), 031403 (11 December 2017). https://doi.org/10.1117/1.JMI.5.3.031403 . Submission: Received: 18 August 2017; Accepted: 14 November 2017
Received: 18 August 2017; Accepted: 14 November 2017; Published: 11 December 2017

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