Development, validation, and relevance of in vivo low-contrast task transfer function to estimate detectability in clinical CT images

PURPOSE: The current state-of-the-art calculation of detectability index (d’) is largely phantom based, with the latest being based on a hybrid phantom NPS combined with patient specific noise magnitude and high-contrast air-skin interface. The purpose of this study was to develop and assess the use of fully-patient-specific measurements of noise and low-contrast resolution, derived entirely from patient images, on d’. METHODS: This study developed a d’ calculation that is patient- and task-specific, employing newly developed algorithms for estimating patient-specific noise power spectrum (NPS) and low-contrast task transfer function (TTF). The TTF estimation methodology used a trained regression support vector machine (SVM) to estimate a fitted form of the TTF given a variance-normalized estimate of the noise power spectrum (referred to as the TTF(NPS)). The regression SVM was trained and tested using five-fold cross validation on 192 scans (4 dose levels x 6 reconstruction kernels x 4 repeats) of a phantom with low-contrast polyethylene insert, and reconstructed with filtered backprojection and iterative reconstructions across 12 clinically-relevant kernels (FBP: B20f, B31f, B45f; SAFIRE: I26f, I31f, J45f with Strengths: 2, 3, 5). To test the low contrast TTF estimation method, the estimated TTF(NPS) measurements were compared to (1) TTF measurements from the air-phantom interface (referred to as the TTF(air), representing the most patient-specific clinical alternative) and (2) TTF measurements from the edge of the low-contrast polyethylene insert (referred to as the TTF(poly)) which represented the gold standard of low-contrast TTF measurement. Patient-specific NPS, patient-specific noise magnitude, and patient-specific low-contrast TTF were further combined with a reference task function to calculate a d’ (according to a non-pre-whitening matched filter model) across 1120 lesions previously evaluated in 2AFC human observer detection of liver lesions. The resulting values were compared to the observer results using a generalized linear mixed-effects statistical model. The correlations between the model and observer results were also compared with previously-reported values (using a hybrid method with phantom-derived NPS and TTF(air)). RESULTS: The TTF(NPS) more accurately represented resolution across the considered reconstruction settings compared with the TTF(air). The out-of-fold predictions of the TTF(NPS) had statistically better RMSE concordance (p < 0.05, one-tailed Wilcoxon ranksum test) to gold standard than the TTF(air) (the alternative, measured from the air-phantom interface). Detectability indices informed by purely patient-specific NPS and TTF were strongly correlated with 2AFC outcomes (p= 0.05, bootstrap resampled corrected paired Student’s t-test). CONCLUSIONS: The results suggest that fully-patient-specific characterization of image quality based on in vivo NPS and low-contrast TTF offer advantages over hybrid methods. The results in terms of detectability index favorably relate to observer detection of liver lesions. The method can potentially be integrated into an automated image quality tracking system to assess image quality across a CT clinical operation without needing phantom scans.