Automated computation of detectability index and generation of contrast–detail curves for CT protocol optimization

Objective. The aim of this study was to develop an automatic method for generating a detectability index (d′)-based contrast–detail (C–D) curve across multiple object sizes and contrasts, and to evaluate its performance under varying tube current settings and reconstruction filter types. Approach. To compute d′ for a given object size and contrast, the task-transfer function and noise power spectrum were obtained from ACR 464 computed tomography (CT) phantom images acquired at tube currents of 80, 120, 160 and 200 mA, using Edge, Lung, and Soft filter types. The task objects were varied in size (1–15 mm) and contrast levels (1–15 HU) with both flat and Gaussian signal types. For each defined task object, d′ was calculated using a non-prewhitening model observer. This process was iterated for every predefined task function across multiple object sizes and contrasts, resulting in a d′ map corresponding to the synthetic low-contrast images. A C–D curve was then generated using a d′ cut-off value defined by the user. For comparison, a separate C–D curve was generated based on visual assessment by five human observers (HOs). Main results. The automated method successfully computed d′ values and arranged synthetic low-contrast images into a grid according to object size and contrast. C–D curves using d′ cut-off values of 3 or 4 most closely reflected HOs performance. For tube current variations, increasing the current led to higher detectability. For filter type variations, the Lung filter resulted in relatively lower detectability compared to the Edge and Soft filters. Significance. An automated method to calculate d′ across a wide range of object sizes and contrasts, and to generate a d′-based C–D curve for CT protocol optimization was developed. The results were consistent with HO trends and effectively captured detectability changes across different imaging parameters.