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

The current state-of-the-art calculation of detectability index (d') is largely phantom-based, with the latest being based on a hybrid phantom noise power spectrum (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'.This study developed a d' calculation that is patient- and task-specific, employing newly developed algorithms for estimating patient-specific 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 NPS (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-prewhitening 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 ).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 root-mean-square error concordance (p < 0.05, one-tailed Wilcoxon rank-sum 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). R² between human detection accuracy and model-predicted detection accuracy were shown to be greater for those measured with patient-specific d' than for the hybrid d' but failed to rise to the level of statistical significance (p ≥ 0.05, bootstrap resampled corrected paired Student's t-test).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 d' 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 computed tomography clinical operation without needing phantom scans.

Duke Scholars

Author Ehsan Abadi Radiology

Author Ehsan Samei Radiology